research about slow internet connection

Are slow internet connections holding back American schools?

Subscribe to the brown center on education policy newsletter, joshua bleiberg joshua bleiberg assistant professor - university of pittsburgh @joshbleiberg.

September 16, 2019

Internet speeds in the United States have for years lagged behind other developed countries. Slow internet speeds have also been an issue for schools, although progress is evident in recent years. In 2012 , 70% of schools lacked internet connections fast enough to support basic administrative and instructional needs (100 KBPS per person), but now only 1.6% of school districts fail to meet that low bar.

Despite this progress, the Federal Communications Commission (FCC) is considering changes to the E-Rate program, which subsidizes internet access in schools across the country. The proposal would cap spending and potentially decrease the funding available to schools. This would threaten recent gains and create additional barriers to helping the few remaining school districts that lack adequate internet connections.

How fast is fast enough?

School internet speeds are measured in bandwidth (kilobytes per second) per student. This approach measures the possible speed assuming simultaneous usage by every student in a school. The table below describes the recommended download speeds for various activities. Some activities like online assessments require only 64 KBPS per student. Other activities involving streaming online video take over 1,000 KBPS per student. In 2014, the FCC set the goal for school internet speeds at 100 KBPS per person. (The FCC speed recommendation is KBPS per student and staff. Throughout this post, internet speeds refers to KBPS per students except with regard to the FCC speed recommendation, where I construct another measure where speed is KBPS per student and staff.) The State Educational Technology Directors Association (SEDTA) recommends at least 250 KBPS per student for online learning models like flipped classrooms or blended learning. This recommendation seems conservative because many students in conventional online learning models are engaged in off-line learning activities (whereas it is likely that nearly all people in a school will use technologies like email or assessments at peak usage times). School districts with internet connections of at least 250 KBPS per student should be sufficient to support virtually all school administrative or instructional needs.

Table 1: Recommended speeds for online activities

How e-rate works.

The E-Rate program subsidizes the cost of telecommunications, telecommunications services, and internet access—as well as internal connections, managed internal broadband services, and basic maintenance of internal connections—for schools and libraries. Each year, the FCC provides nearly $4 billion to schools and libraries. Schools must apply to receive these funds and report the current upload and download speeds. E-Rate will cover between 20% to 90% of a school district’s expenses related to internet access. School districts with greater numbers of impoverished and rural students receive more funding.

EducationSuperHighway (ESH) is a nonprofit organization that provides technical support to school districts seeking to upgrade their internet access. ESH uses publicly available data from E-Rate and publishes information about the available bandwidth in school districts throughout the country. In their E-Rate application, school leaders likely report speeds advertised by internet service providers that can differ significantly from actual speeds. ESH puts in a considerable effort validating these publicly reported values with speed tests from school internet connections. The data include internet speeds from the vast majority of traditional public school districts (~91%). School districts that do not apply for E-Rate or take ESH’s speed test are excluded from these data. I combined the ESH data with school demographic characteristics (Common Core of Data), student poverty (Small Income Area Poverty Estimate), and school spending (Annual Survey of School System Finances) from 2017, the most recent year of available data.

Does E-Rate benefit students?

The available research suggests the relationship between internet communication technologies and student achievement is weak. Vigdor, Ladd, and Martinez (2014) found that gaining access to high-speed internet in the home was associated with a small but significant decrease in academic outcomes. Angrist and Lavy ( 2002 ) evaluated Israeli efforts to implement “computer-aided instruction” and found no association with student test scores. Evaluations of the E-Rate program itself have found null effects on student achievement. Goolsbee and Guryan ( 2006 ) found no evidence of a correlation between receipt of E-Rate funds in California and student test scores. Hazlett, Schwall, and Wallsten ( 2019 ) found an insignificant correlation between SAT scores and the receipt of E-Rate funds. Given this research, slow internet speeds are likely not harming student outcomes. However, schools need access for basic administrative purposes, to conduct online tests, offer support instruction, and communicate with parents.

The distribution of internet speeds

The variance of school internet connection speeds is quite large. The distribution of internet speeds has an extreme right skew. The average district has 1,484 KBPS per student and the median is 654 KBPS per student. The figure below visualizes the log of the KBPS per student by school district, where the red line indicates the median internet speed (654 KBPS per student). About 5% of school districts appear to have very fast internet speeds (greater than 5,000 KBPS per student) capable of supporting any technology that relies on the internet. At the other end of the distribution, about 15% of school districts fall below the 250 KBPS threshold recommended by SEDTA needed for online learning. This includes several hundred districts that have connections barely suitable for light email use, and 33 school districts (about one-third of a percent) do not have internet speeds capable of using online multiple-choice assessments.

The map below displays the average school internet speed by county. Lighter greens indicate slower average internet speeds per student while darker blues indicates faster connections. The map visualizes the wide variation in school internet speeds. In particular, differences across states and urbanicity are present. Internet speeds in Appalachia appear quite low. However, states including Arkansas, Oklahoma, South Dakota, and North Dakota that have many school districts in rural communities have higher-than-average school district internet speeds. One possible explanation for heterogeneity in speeds across states is proximity to the internet “back bone.” Internet access close to these underground cables could be both faster and less expensive. It’s also likely that state policies supporting internet technology in schools that supplement E-Rate could account for some of the variance in school internet speeds.

Figure 1: Average KBPS per student by county

Internet speeds in rural school districts were, on average, faster than urban areas. In fact, 18% of school districts in urban areas had average internet speeds below 250 KBPS per student. These results should be read with considerable caution due to the strong likelihood of selection bias. None of the school districts missing from the dataset received E-Rate funds and the vast majority of school districts not observed in the dataset are in rural areas. School districts that are not observed are very likely to have slower internet speeds on average because none have E-Rate funds to boost their speeds. In addition, the missing school districts in rural areas are more likely to have internet connections that cost more and are slower.

School districts with more at-risk students and fewer resources typically have slower internet connections. Internet speeds appear to be negatively correlated with the number of impoverished students in the school district. About 29% of school districts with the most impoverished students (top quintile) do not have average internet connections greater than 250 KBPS per student. The association between per student expenditures and internet speed is positive. Only 4% more school districts in the top quintile of per-student spending have average internet speeds greater than school districts in the first quintile. The race/ethnic makeup of a school district also appears to be related to internet speeds. The percent of black students in a school district is negatively correlated with internet speed, and the number of white students is positively correlated with internet speeds: 91% of school districts with relatively few black students (0% to 20%) have internet connections faster than 250 KBPS per student, while 78% of school districts that are 81% to 100% black have average internet speeds that fast.

The future of E-Rate

American internet speeds will likely lag behind other countries into the future. Lack of competition and investment in the last mile of infrastructure remain systematic issues that E-Rate is ill-suited to address. Policymakers should strive to achieve two goals. First, protect the dramatic increases in school internet speeds that have occurred over the past five years. Second, state and federal policymakers should strive to protect the success of E-Rate and target the few remaining districts with very slow internet connections.

The proposal to cap E-Rate spending is only in the early stages, and there’s not sufficient information to judge at this moment whether it would threaten current progress. The original purpose of E-Rate according to the FCC was to ensure that schools have access through discounts and grants ( see section X ). But capping spending seems insufficient for addressing the avowed goals of dealing with fraud in the E-Rate program. Stepping up enforcement or requiring more-detailed reporting would better address that problem. If a cap prevented school districts from getting E-Rate funds that they currently receive, then it would slow down average internet speeds. School district demand for broadband is somewhat close to supply . School districts use E-Rate to pay not just for equipment like routers and Wi-Fi, but also monthly internet connection fees. Any cuts to E-Rate could directly influence a school’s ability to afford internet access.

A notable concern: It appears school districts with slow internet speeds tend to enroll more at-risk students. Given the evidence that internet connections in the home and school are weakly related to student outcomes, it’s unlikely that slow internet speeds are preventing improvements in the classroom. However, there is a baseline level of internet access that schools need to function for administrative needs (e.g., email, Chrome applications, Dropbox) and for educational purposes (e.g., online assessments, streaming video). The E-Rate program benefits partners in all sectors (schools and internet service providers), and the FCC should continue this successful policy rather than changing course and risking recent gains in school internet speeds.

Effects of Lack of Internet Access in the Academic Performance of STEM Students

Related Papers

International Journal of Academic Research in Business and Social Sciences

Sazili Shahibi

Venkateswar Meher

The present study investigates the impact of using internet on the academic performance of UG students studying in higher educational institutions. For the study, casual comparative research method was employed. The main purpose of the study was to examine the impact of using internet on the academic achievement of UG students and to study the significant difference between the academic achievement scores of internet users and non-users in relation to Arts, Science, Commerce streams. Near about 450 students studying in higher education institutions of Sundargarh district, Odisha were selected as the sample of the present study. The data was collected with the help of Achievement Score Card and analyzed by applying ‘t’ test. The result showed that there was significant difference between the achievement scores of internet user and non-users, the achievement scores of internet users were higher as compared to the non-users, also significant different was found among the internet users...

International journal of scientific research in computer science, engineering and information technology

Farah Quraishi

European Scientific Journal, ESJ

NIMRA NAWAZ

It is impossible to ignore the fact that the internet is the basic need of today’s technological world. Different research shows that individuals across the globe are using information and communication technologies (ICTs) to get the most up-to-date information from the internet. Similarly, university students also spend a lot of time on the internet researching and collecting material relevant to their research topics. Although different research has been conducted on the usage of the internet in different countries, research about Pakistani students is still limited. The major goal of this research was to see how internet usage affects students' academic performance particularly when it comes to increasing their grades. The objective of this research was to determine the type of internet use and the impact of the internet on students' academic achievement. Arts faculty from the University of Punjab Pakistan was selected as the universe of the study and 5 departments of art...

Nikka Corañez

Conference Proceedings of the 4th International Conference for Science Educators and Teachers, Khon Kaen University, Khon Kaen, THAILAND

Dharel Acut

This study was carried out to determine if the students' academic performance is affected by their Internet usage. The study was conducted among three hundred eighty six (386) undergraduate students within Mindanao State University-Iligan Institute of Technology. The researchers framed a structured interview and a questionnaire yearning to gather all the information needed. The data collected from the respondents pointed out that there was no significant relationship between the respondents' academic performance and their Internet usage. Findings of the study also reveal that there was no significant relationship between the respondents' academic performance and the place where they have accessed the Internet. Another finding shows that there was no significant relationship between the respondents' academic performance and their purposes in using the Internet such as for Gaming, Social Networking, News, and Entertainment. There was, however, a significant relationship between the respondents' academic performance and their usage of Internet for academic purposes. From the results, the researchers recommend that Educators may encourage students to use the Internet by giving them assignments, projects, and online quizzes.

Ethics and plagiarism in scientific communication

Revista Comunicar , Josep M Duart , Isidro Marin-Gutierrez , Juan Torres

The use of technology is changing the way things are done, including the work in universities where the teaching and learning process are changing, and it is required to know the effect of technology on student achievement. In this research work, we present the influence of Internet use on academic success of students from five universities in Ecuador. A random sample of 4,697 people was got up and categorized in two groups: the use of Internet in academic activities and entertainment, using factor analysis and cluster analysis; the resulting categories were used as independent variables in multinomial logistic regression model which are seeking to determine if the use of Internet has impacted on academic success. The results show that people who perform interactive activities with peers and teachers or use in a balanced way the different internet tools tend to have more academic success than those who only seeks information. Regarding to the use of Internet in entertainment, a positive impact was found on academic achievement. Students who download audio, video and software, and students who use all the entertainment possibilities show less likely to fail than who using minimally Internet. In terms of gender, it has different effects for entertainment and academic purposes.

Jurnal Aplikasi IPTEK Indonesia

Sapran Efendi

The emergence of the internet has a major effect on human life, including in education. The internet contributes to academics so that it has a very important role for student learning success. This research aims to describe the contribution of internet utilization as a learning resource towards the student learning outcomes at SMA Negeri 2 Padang. This study used a descriptive quantitative research method, and the data were analyzed by using the simple linear regression. The population of this research was 920 students of SMA Negeri 2 Padang, and the sample chosen was 279 students selected by using the proportional random sampling technique. The research instrument used was a scale of internet utilization with a Likert model. The findings of this research showed that the use of the internet as a learning resource contributes towards the student learning outcomes at SMA Negeri 2 Padang with an effective contribution 40.4%.

Impact of Internet Usage on the Academic

Sunday Nyeche Okoro

This research work is a study on the Impact of Internet Usage on Academic performance of undergraduate students using University of Abuja, Nigeria as a case study. The research design adopted was the survey method using questionnaires as the instrument for data collection. The population of the study was the entire students of the University of Abuja, with students randomly selected from each level making a total sample of 375 students whom were issued questionnaires. Out of the 375 questionnaires distributed, 200 were dully responded to and returned to the researcher. Data collected were analysed using inferential statistics, frequency table and percentage. The findings revealed that Internet is one of the beneficial tools in this era of IT not only for business but for academic point of view and enhances the skill and capability of students which assist them in studies and professional life. It also revealed that some of the problem encountered in the usage of Internet includes lack of computer skills, slow internet server and problem of paying for online services. The study recommends that the university should embark on Information literacy/sensitization programme for student's in order to effectively harness the various web based electronic resources it is subscribed to.

European Journal of Education Studies

Sunday Obro

Abstract: The study investigated the impact of Internet access and usage on students’ academic performance in Social Studies. The study adopted a correlational research design. The population consisted of 55,303 male and female JSS II students from which 576 students were selected. The instrument for the study was the questionnaire and students’ academic records. The data collected were analysed using correlational statistics. The result of the study revealed that there is significant relationship between internet usage for academic work and students’ academic performance; there is significant relationship between internet access and usage and students’ assignment submission; there was significant relationship between time spent on the internet and students’ academic performance. It was recommended that students should use the internet to enhance their academic performance of which they should restrict themselves from non-academic purposes; teachers should evolve regulations to guide the extent of Internet usage to avert over dependency among students.

Everything You Need to Know About Slow Internet Speeds

Our crummy connections are the biggest tech headache in the pandemic. Here’s a comprehensive guide to what to do about them.

By Brian X. Chen

Restricted to our homes for months now, many of us have been putting up with a persistent annoyance: a lousy internet connection.

When we are working, a video call with colleagues becomes pixelated, with delayed audio. When we are relaxing, movies and video games take ages to download. In the worst cases, the connection drops altogether.

As people have hunkered down to contain the spread of the coronavirus, average internet speeds all over the world have slowed . Some broadband providers are feeling crushed by the heavy traffic. And dated internet equipment can create a bottleneck for our speeds.

Even the most tech savvy are affected. Keerti Melkote, the founder of Aruba Networks, a division of Hewlett Packard Enterprise that offers Wi-Fi products for businesses, said that in recent weeks, his DSL service from AT&T had dropped periodically. He waited several days for a technician to arrive and is now contemplating subscribing to Comcast for a second internet connection.

“I had three or four days of calls, and I had to go find a particular spot in my house where I had better coverage,” Mr. Melkote said.

At the beginning of the pandemic, my internet also became unbearably slow and suffered several outages. So I asked experts to explain what’s causing our internet problems — and the different remedies.

First, diagnose the problem.

What’s causing your slow speeds — your internet provider or your equipment at home? Here’s a method to figuring that out.

Download an internet speed test app on your phone, like Speedtest by Ookla (free for iPhones and Android phones ).

Stand near your router and use the app to run a speed test.

Move to a room farther away from the router and run the speed test again.

Compare the results.

Less than 15 megabits a second is pretty slow. Speeds of about 25 megabits a second are sufficient for streaming high-definition video; more than 40 megabits a second is ideal for streaming lots of video and playing video games.

If the speed test results were fast near your Wi-Fi router but slow farther away, the problem is probably your router, said Sanjay Noronha, the product lead of Google’s Nest Wifi internet router. If speeds were slow in both test locations, the issue is probably your internet provider.

If it’s your router, here’s what to do.

If you have pinpointed that the problem is your router, the bad news is that you may have to buy new equipment. The good news is that there are many approaches to improving your Wi-Fi connection.

Start by asking yourself these questions:

How old is my router? If it’s more than five years old, you should definitely replace it. In 2015, the Federal Communications Commission removed restrictions that had limited the wireless transmission power of Wi-Fi routers, allowing new routers to be 20 times more powerful than they were before. Upgrading to a newer router will probably be one of your most life-changing tech purchases.

Where is my router placed? Ideally, your router should be in a central location in your home so that the signal covers as many rooms as possible. In addition, your router should be out in the open, like on top of a shelf, not hidden inside cabinets or under a desk, to beam a clear signal. You should also avoid placing the router near objects and materials that cause interference, like large fish tanks and metal.

How big is my home? If you have a home with multiple stories and lots of rooms, and your Wi-Fi is weak in some areas, the best solution is to buy a so-called mesh network system. It’s a system of multiple Wi-Fi access points, including a main router and satellite hubs, that lets you connect multiple wireless access points together to blanket your home with a strong internet connection.

My favorite mesh systems are Google Wifi and Amazon’s Eero , which start at $99 for a single router and can be bundled with additional access points. In general, I recommend mesh systems even for smaller homes, because they are fast and very easy to install.

Are my other devices slowing down my connection? Gadgets with slower internet technology can slow down speeds for all your other devices.

For example, the iPhone 5 from 2012 uses an older-generation Wi-Fi standard. Newer iPhones, from 2014 and later, use a faster wireless standard.

Let’s say you own a new iPhone and your teenager owns the iPhone 5. If your teenager begins downloading a video on the iPhone 5 and then you start downloading something on your iPhone, the older phone will take longer to finish before the signal frees up for your phone to download at maximum speed.

As a remedy, many modern Wi-Fi routers offer settings that can give specific devices a priority for faster speeds . Consult your router’s instruction manual for the steps. In this hypothetical example, you would want to give your new iPhone top priority and move your teenager’s old iPhone to the bottom.

Are my neighbors slowing down my connection? In apartment buildings crowded with gadgets, the devices’ signals are fighting for room on the same radio channels. You can see what radio channels your neighbors’ devices are using with scanning apps like WiFi Analyzer . Then consult your router’s instruction manual for steps on picking a clearer radio channel.

This step is tedious, and many modern routers automatically choose the clearest radio channel for you. In general, replacing an outdated router is the most practical solution.

If it’s your service provider, there’s not much to do.

If you have determined that your internet provider’s service is the root of the issue, your only option is to call your internet service provider and ask for help.

When you call, ask a support agent these questions:

Why are my speeds slow? Occasionally a support agent can analyze your internet performance and make changes to speed up your connection. This rarely happens, and more often a technician will need to pay a visit.

Does my modem need to be replaced? The modem, which is the box that connects your home to the internet provider’s service, also can become outdated and occasionally needs to be replaced. If the support agent confirms the modem is old, you can schedule an appointment for a technician to install a new one.

Or you can buy your own modem and call the internet provider to activate it. Wirecutter, our sister publication that tests products, recommends modems from Motorola and Netgear , which cost about $80 to $90.

Can I buy faster speeds? Your provider may offer packages with more bandwidth meant for higher-quality video streaming and faster downloads. Ask about your options.

As a last resort, you can turn to backups. Many modern phones come with a hot spot feature, which turns the device’s cellular connection into a miniature Wi-Fi network. (Apple and Google list steps on their websites on how to use the hot spot feature on iPhones and Androids .)

Whatever you do, be patient. In these trying times, everything takes longer.

As for me, I confirmed my slow speeds were related to my internet provider, Monkeybrains. I called to report the issue, and after more than a month, a technician replaced the antenna on our roof. Now my speeds are even faster than before the pandemic, so it was well worth the wait.

Brian X. Chen is the lead consumer technology writer. He reviews products and writes Tech Fix , a column about solving tech-related problems. Before joining The Times in 2011, he reported on Apple and the wireless industry for Wired. More about Brian X. Chen

Tech Fix: Solving Your Tech Problems

Switching From iPhone to Android: Even if you manage to ditch your iPhone, Apple’s hooks are still there .

Trying Meta’s Smart Glasses: What happens when a columnist and a reporter use A.I. Ray-Bans to scan groceries, monuments and zoo animals? Hilarity, wonder and lots of mistakes ensued .

Ditch Your Wallet: Using your phone as a digital wallet is attainable , but it requires preparation and some compromise.

Managing Subscriptions: The dream of streaming — watch what you want, whenever you want, for a sliver of the price of cable! — is coming to an end as prices go up. Here’s how to juggle all your subscriptions and even cancel them .

Apple’s Vision Pro: The new headset teaches a valuable lesson about the cost of tech products: The upsells and add-ons will get you .

Going Old School: Retro-photography apps that mimic the appearance of analog film formats make your digital files seem like they’re from another era. Here’s how to use them .

Spring 2020

Lack of internet connections deepen inequity in education and health care during COVID-19

March 19, 2021

By Liam Bohen-Meissner Medill Reports

Access to virtual education and telehealth underscore the digital divide in Chicago and elsewhere exacerbated by the onset of the COVID-19 pandemic. When coupled with limited in-person activity, a lack of broadband internet potentially forces millions of students to fall behind in school and millions of people to forgo medical treatment. Some 3.7 million households in the United States lack consistent and/or stable internet connection.

Bridging the digital divide will take time, resources, and a partnership between the private and public sectors. This podcast looks at the needs and solutions to optimize digital learning as viewed by policymakers, nonprofits, teachers, doctors, internet users and game changers such as Chicago Connected .

Public-private partnerships bridge the digital divide in education

Resources and Information for Meeting the Digital Divide Challenge

Kids First Chicago Digital Equity in Education in the Coronavirus Era report
Information on Chicago Connected
EducationSuperHighway website
Information on public internet referendum

The jury is still out on the impact of the digital divide on telemedicine.

COVID-19 not only revealed the damage caused by the digital divide in education but demonstrated how the digital divide can impact medical care as well. Doctors and experts are divided on whether telemedicine, lauded as a safe and reliable option for in-person visits, leads to greater inequity because of the digital divide. Telemedicine became popular during the COVID-19 pandemic. It is t he use of a phone call or video chat in lieu of attending an in-person doctor’s appointment.

Some practitioners such as Dr. David Ansell of Rush University Medical Center believe telemedicine has the potential to reduce inequities among patients. The increased accessibility of a phone call or video chat can eliminate other obstacles such as taking time off of work or traveling long distances for an in-person visit.

Other medical experts like Dr. Srinath Adusumalli of Penn Medicine at the University of Pennsylvania, however, published research that highlights the digital divide and the inequities it creates in telehealth.

“I do think that care via this modality [telehealth]…are the way of our health care delivery systems future,” Adusumalli said. “But it’s the work of our time, so to speak, right now and in the health care field to be able to think about how best to integrate those into the care delivery process.”

Telehealth less of an option for some communities

The Pew Research Center’s 2019 data shows that 96% of Americans own a cell phone and 81% have a smartphone. This means telehealth via phone calls and phone apps are an option for most Americans. But to access video chat appointments via services like Zoom or a provider’s mobile app, patients need a computer or smart device that can connect to the Internet through WIFI or data.

Yet it is estimated that approximately 42 million Americans do not have access to broadband internet according to an article from the American Heart Association . An observational study conducted in 2019 , and research from the Pew Research Center show that communities generally most impacted by the digital divide are women, older people, people with disabilities, lower income individuals, people of color, immigrants, and those residing in rural areas.

A lack of digital literacy or technological literacy can hinder patient’s ability to access telehealth as well. People may have difficulty navigating a medical system’s app or website making telehealth a difficult option, Adusumalli explained.

He co-authored a study out of the University of Pennsylvania last year that examined 148,402 patients scheduled for primary care or medical specialty ambulatory telemedicine visits. Examples of primary care visits include annual check-ups or physicals while specialty ambulatory care are visits with medical specialists such as cardiologists or neurologists.

He found during the early part of the pandemic that older people and those who do not use English as their first language tended to complete fewer telemedicine appointments. Patients who were older, female, Black, Latinx, and of a lower household income tended to use video less for their telemedicine appointments.

A lack of or lower rate of access for particular communities can impact the quality or type of care they receive. While a phone call for example, can suffice for certain routine visits, a video chat can provide a greater array of services.

Adusumalli said video visits allow clinicians to see how a patient is doing physically. Clinicians have developed creative ways to conduct portions of a physical exam as well. But video chats also provide a window into a patient’s home life which can be helpful in identifying safety issues or reviewing how to take medications.

“So I’ve had a number of opportunities where being able to review that with patients and identify either medication errors or missing medications, for example,” he said. “So that’s where video, I think adds to the conversation.”

Telehealth as a means to eliminate certain inequities

Overall, Ansell said that for visits with many of his patients, telehealth, even via a phone call, is a great alternative.

“Except for looking at someone in the eye, it’s not like you could do a physical exam over a video, you may be able to look at a rash, but someone can send you a picture of that as well,” he said. “So I actually think that the inequities are actually reduced.”

He acknowledges that these visits are mainly for check-ins and other low stakes visits but emphasized the ease of access for patients.

“I mean, rather than looking at it as a barrier, which I understand your questions, what about a great door opener?” he said. “Cause in the reach of your phone is your doctor.”

Adusumalli agreed, saying that because people often have to take time and resources to travel to an appointment, telehealth could cut that out of the equation.

“There are lots of barriers for a patient coming to meet us where we’re at,” he explained. “That could range from transportation issues, having to take time off from work, child care, all of these are real barriers to accessing care and coming to scheduled appointments, some of which can be averted by using telehealth.”

Both Adusumalli and Ansell believe telehealth will become more common in the future. While the jury is still out on whether telehealth reduces or creates more inequities, ensuring it is accessible to all will become far more necessary in the future.

Liam Bohen-Meissner is a health and politics reporter at Medill. You can follow him on Twitter at @lbmeissner .

MEDILL SCHOOL OF JOURNALISM, MEDIA, INTEGRATED MARKETING COMMUNICATIONS 1845 Sheridan Road Evanston, IL 60208-2101

Research article
Open access
Published: 21 May 2021

The disconnected: COVID-19 and disparities in access to quality broadband for higher education students

John Cullinan 1 ,
Darragh Flannery 2 ,
Jason Harold 1 ,
Seán Lyons 3 &
Dónal Palcic 2

International Journal of Educational Technology in Higher Education volume 18 , Article number: 26 ( 2021 ) Cite this article

40k Accesses

56 Citations

43 Altmetric

Metrics details

The COVID-19 pandemic forced many higher education institutions (HEIs) across the world to cancel face-to-face teaching, close campus facilities, and displace staff and students to work and learn from home. Given the persistent nature of the pandemic, many HEIs have continued to deliver courses online and/or use a blended learning approach. However, there are concerns around differences in student access to digital learning resources while at home, including high quality broadband connectivity. This is important, since variation in connectivity may impact the type of online/blended model that faculty can deliver or constrain student engagement with online content. In this context, this paper combines national data on the domiciles of students enrolled in Irish HEIs with detailed spatial data on broadband coverage to estimate the number of higher education students ‘at risk’ of poor access to high quality internet connectivity. Overall it finds that one-in-six students come from areas with poor broadband coverage, with large disparities by geography and by HEI. It also finds that students from the poorest broadband coverage areas are more likely to be socioeconomically disadvantaged. As a result, this paper recommends that HEIs use their detailed registration data to help identify and support at-risk students. In particular, the results suggest that some HEIs may need to prioritise access to campus facilities and services to less well-off students living in poor broadband coverage areas.

Introduction

In early 2020, the COVID-19 pandemic forced many higher education institutions (HEIs) across the world to cancel face-to-face teaching, close campus facilities, and displace staff and students to work and learn from home. For example, the European University Association (EUA) estimated that 90% of HEIs in Europe ‘went online’ at this time, for all or most of their classes (Gaebel, 2020 ). Given the persistent nature of the pandemic, and the potential threat of further waves of the virus, many HEIs decided to continue to deliver courses online and/or use a blended learning approach. Evidence from the United States (US) suggests that the majority of colleges have adopted this approach (Staff, 2020 ), while a similar situation exists in numerous other countries, including the United Kingdom (UK), Australia, and Ireland (Bothwell, 2020 ; Davies, 2020 ; McGuire, 2020 ). While these modes of delivery have existed within the higher education sector for a number of years, the scale of such change is unprecedented and raises a number of important issues.

One such issue is the potential difference in access to digital learning resources for students that reside at home, rather than on or near campus, in an online delivery context (Raes et al., 2019 ). Such a divide may be driven by a range of factors, including gaps in access to appropriate equipment, such as a laptop or desktop personal computer (PC), a suitable home environment to learn/study in, or the digital literacy skills required to engage with online learning (Silva et al., 2018 ). Furthermore, differences in the quality of broadband connectivity Footnote 1 for students living at home, as opposed to on campus, is likely also an important consideration in this potential divide (Rasheed et al., 2020 ). Given the catchment areas of many HEIs cover both urban and rural areas, variation in connectivity may impact the type of online/blended model that staff can deliver, or constrain certain groups of students from fully engaging with online-based content. Within this context, this paper considers college students in Ireland at risk of poor access to high quality internet connectivity due to poor broadband coverage.

With the potential to decrease temporal and spatial constraints relative to traditional higher education offerings, the number of students enrolled in online learning in higher education globally has grown significantly in recent years (Panigrahi et al. 2018 ). Nonetheless, prior to the pandemic, face-to-face delivery constituted the vast majority of student contact time. Despite this, there were still concerns around broadband connectivity. For example, in the US, Gonzales et al. ( 2018 ) estimated that 20% of college students had difficulty maintaining access to technology, including internet connectivity. With the sudden move to emergency online delivery and the widespread closure of campus facilities, the issue has come into sharp focus.

Despite this move, there is limited evidence regarding the impact of differences in broadband access or speeds on learning outcomes in online education at a large scale. Two notable exceptions are Sanchis-Guarner et al. ( 2021 ) and Dettling et al. ( 2018 ). The former uses data on test scores of 14-year-olds in the UK and finds that increasing broadband speed by 1 Mbps increases test scores by 1.37 percentile ranks, while the latter uses US data to show that students with broadband access in their postal codes perform better on the SAT and apply to a larger set of colleges. To the authors’ knowledge, similar studies in a higher education setting do not exist. However, studies such as Skinner ( 2019 ), Rasheed et al. ( 2020 ), Raes et al. ( 2019 ), and Zydney et al. ( 2019 ) all highlight the technological challenges, such as access to high-speed broadband, that can impact on student and teacher engagement with online education, particularly with synchronous-based material.

Given the important role that student engagement likely plays in academic success and student satisfaction, particularly for first year students and in online learning environments (Kahu, 2013 ; Kahu et al., 2020 ; Paulsen & McCormick, 2020 ), this raises the issue of potential differences in the quality or type of delivery students may receive in the current context as a result of unequal broadband access. Furthermore, using survey data from 78 centres for teaching and learning across 23 countries in Spring 2020, Naffi et al. ( 2020 ) identified bandwidth issues as problematic for students in certain aspects of their learning experience, such as sharing files or synchronous classes.

In addition to these studies, student survey data from the UK indicated that 7% of students reported having insufficient access to the internet, a figure that rises to 12% for those from lower socioeconomic households (Montacute & Holt-White, 2020 ). A separate survey found that 56% said they lacked access to appropriate online course materials, with 9% “severely” impacted (Office for Students, 2020 ). In Ireland, the context for this paper, based upon their experience of online learning in March 2020, 21% of third-level students indicated that access to reliable Wi-Fi was a key requirement to help improve their learning experience going forward (Union of Students in Ireland, 2020 ). Footnote 2 Overall, these recent student surveys, along with the previous academic research, help motivate the research questions.

Research questions

From both a policy and HEI management perspective, it is important to understand variation in the quality of home broadband connectivity and to identify groups of students that may be at risk in terms of a pedagogical digital divide. To do so, this paper uses geographic information systems (GIS) techniques to examine national data on the domiciles of students enrolled in Irish HEIs. It combines this information with spatial data on broadband quality from Ireland’s National Broadband Plan (NBP), which allow the number of college students ‘at risk’ of poor access to high quality internet connectivity as a result of coverage issues to be estimated. In considering these ‘disconnected’ students, the paper examines disparities by geography, HEI, and socioeconomic background.

The specific research questions (RQs) that are addressed in this paper are as follows:

RQ1: How many and what proportion of college students come from areas with poor broadband coverage and are therefore at risk of poor access to high-speed broadband?

RQ2: Are there significant differences in the proportions of college students from poor broadband coverage areas by geography and by HEI?

RQ3: Are college students from lower socioeconomic backgrounds more likely to come from poor broadband coverage areas?

In addressing these research questions, the paper is structured as follows: “ Literature ” section sets out the relevant extant literature, “ Institutional and policy context ” section describes the context for the study in more detail, “Materials and methods” presents the data and methods, while “ Results ” section discusses the main empirical results. “ Discussion and implications ” section summarises the implications of the results and findings, “ Conclusion ” section concludes, while “ Limitations and future research directions ” section discusses limitations and future research directions.

Due to the recent nature of the crisis caused by the COVID-19 pandemic, there are few empirical studies that examine the impact of broadband coverage on access to online education, at any level of education. One notable exception is Bacher-Hicks et al. ( 2021 ), which provides stark evidence of the education digital divide in the US during the COVID-19 lockdown period. Using high-frequency Google search intensity data for online learning resources across 210 different regions, the study shows that areas of the country with higher income levels, better internet coverage, and fewer rural schools saw significantly larger increases in search intensity relative to less advantaged areas. It stresses the importance of additional support for students in low socioeconomic status (SES) areas and rural communities if inequalities in access to, and engagement with, online learning resources are to be reduced (Bacher-Hicks et al. 2021 ).

In Ireland, a recent study by Mohan et al. ( 2020 ) on the impact of the COVID-19 pandemic on second-level education finds that almost half of schools surveyed reported issues with a lack of access to high-speed broadband and/or a lack of access to appropriate digital devices for their students. This figure increases to approximately 58% for disadvantaged schools and schools in catchment areas characterised by lower than median household incomes (Mohan et al., 2020 ). The survey also finds a significant digital divide in relation to the use of live online video classes. For example, in schools located in areas of lower quality broadband coverage, 62% reported delivering all or most classes live online, compared to 90% of schools located in areas with good broadband coverage. Moreover, in schools located in areas characterised by lower incomes, just under half reported delivering all or most classes live online, compared to almost two-thirds of schools located in higher income areas (Mohan et al., 2020 ).

Further empirical studies that examine the relationship between the quality of broadband coverage and access to online education during the current pandemic have yet to be published. However, there are a number of pre-pandemic studies that focus on important and related issues, such as: (1) access to quality broadband and appropriate equipment for students; (2) digital inequality driven by socioeconomic factors; and (3) the impact of online learning on student outcomes. While not directly comparable to the research question within this paper, they nonetheless provide important context.

For example, Raes et al. ( 2019 ) and Rasheed et al. ( 2020 ) both provide systematic reviews of synchronous hybrid learning and the online aspect of blended learning respectively. Raes et al. ( 2019 ) suggest grounds for cautious optimism about synchronous hybrid learning in creating an engaging learning environment relative to fully online, but also acknowledge the technological challenges, such as connectivity issues, that may present in such an environment. Rasheed et al. ( 2020 ) also highlight the issue of quality broadband (under the heading of technological sufficiency challenges) as one of the main student challenges, but also a potential challenge for staff in a blended learning environment using video content.

In another study, Skinner ( 2019 ) uses data from the US national broadband plan to examine the relationship between access to high-speed broadband and the number of students at public universities and community colleges who opt to take some of their courses online. It finds that increases in broadband speed at the lower end of the speed spectrum are positively associated with the number of students who take some of their courses online and emphasises the importance of considering broadband speed in improving the access of students to courses with online content.

While the digital divide in terms of broadband connectivity is obviously important, there can also be gaps in terms of access to appropriate equipment, such as a laptop or desktop PC, the right environment to work in from home, and the digital literacy skills required to engage with online learning. For example, students from lower SES families are less likely to have access to broadband, less likely to have access to a computer, and less likely to have an appropriate learning environment in their home, compared to students from higher SES families (Lamb et al., 2020 ; Silva et al., 2018 ). They are also more likely to have relatively weaker information and communications technology (ICT) skills, as well as the capacity for working independently with ICT (Lee, 2017 ; Ortagus, 2017 ; Stich & Reeves, 2017 ).

With regard to broadband uptake, Silva et al. ( 2018 ) used census block level fixed broadband availability and broadband adoption data, along with various demographic and socioeconomic variables, to examine the determinants of broadband adoption in the US. The study finds that the broadband availability rate is the most significant factor affecting broadband adoption rates in non-metropolitan areas, while household income and educational attainment play a more significant role in metropolitan areas. Interestingly, it also highlights the importance of shifting the focus of future research away from broadband availability and adoption, towards considering the stability and speed of broadband connections in different geographical areas (Silva et al., 2018 ). This is particularly important in the context of this paper, since it is not just the availability of broadband that matters for students. Rather, it is broadband quality or performance, defined in terms of upload/download speeds and latency, which is a critical factor for learners engaging with many of the applications and technologies used for synchronous sessions.

In terms of the extant empirical literature on the impact of online learning on student engagement and educational outcomes, this has generated mixed and contested results (Paulsen & McCormick, 2020 ). For example, Xu and Jaggars ( 2013a ) use a large administrative dataset from a state-wide system of 34 community and technical colleges in Washington State in the US to estimate the impact of online versus face-to-face delivery on academic performance. The study, using various approaches and model specifications, indicates that online delivery has a significant negative impact on both course grade and course persistence. On the other hand, Figlio et al. ( 2013 ) provide experimental estimates of the effects of online versus face-to-face instruction on student learning and find only modest evidence in favour of face-to-face delivery.

While there is no clear-cut consensus as to the efficacy of online learning versus face-to-face delivery, Xu and Jaggars ( 2013b ) argue that the gap between online and face-to-face outcomes may be more significant for less-advantaged cohorts. The authors suggest that gaps in outcomes may therefore be higher for colleges with higher proportions of disadvantaged students and less obvious for institutions that serve more socially advantaged students with better prior academic ability. Farrell and Brunton ( 2020 ) also highlight how successful online student engagement is influenced by “a number of psychosocial factors such as peer community, an engaging online teacher, and confidence or self-efficacy and by structural factors such as lifeload and course design”.

While many of the studies discussed in this section relate to the pre-pandemic online education experience of relatively small numbers of students, the findings are nonetheless highly relevant to the current experience of a rapid transition to mass online learning. In particular, they underline the importance of access to high-speed broadband in an online learning environment.

Institutional and policy context

Higher education landscape.

HEIs in Ireland include universities, technological universities (TUs), institutes of technology (ITs), and colleges of education (CEs), as well as a small number of other public and private colleges. In 2018/19 enrolments totalled 228,503, with the majority of those (186,174; 81%) at undergraduate level (Higher Education Authority, 2020 ). Of those enrolled, 55% were in the university sector, 40% were in TUs/ITs, with the remaining 5% in other colleges (Higher Education Authority, 2020 ). Health and humanities courses at honours bachelor degree level are more common in the university sector, while a focus on engineering, construction, and care courses at both ordinary and honours bachelor degrees is more common in TUs and ITs. Compared to universities, TUs and ITs offer more part-time and flexible courses, with a larger proportion of mature and disadvantaged students, while universities offer more postgraduate opportunities (Higher Education Authority, 2019 ). Flannery and Cullinan ( 2017 ) present further details of the Irish higher education sector.

From a spatial perspective, universities and CEs in Ireland tend to be located in larger urban centres, whereas TUs/ITs are more geographically dispersed and smaller in size on average (Additional file 1 : Figures A.1 and A.2). A substantial body of research has examined student mobility and enrolment patterns in Ireland. Footnote 3 In general, these studies have found that proximity to a HEI strongly influences where a student enrols and these ‘localised’ patterns of progression to HEIs are likely important in the context of understanding disparities in access to quality broadband services. In terms of financial aid, the Irish State provides maintenance grants to students who meet certain criteria based on parental income levels and geographic distance from their chosen HEI. It is also relevant to note that the most recent equity of access plan in the sector, the National Plan for Equity of Access to Higher Education 2015–2019 (Higher Education Authority, 2015 ), while not focusing on potential digital divides specifically, acknowledges the potential role that geographic factors may play in higher education accessibility.

On March 12th 2020 all HEI facilities in Ireland, including libraries, offices, classrooms and labs, were closed to staff and students with the remainder of the spring semester’s teaching delivered remotely. With student accommodation almost entirely vacated, this emergency shift to online teaching and alternative assessment resulted in the vast majority of students learning from home. For the 2020/21 academic year, a fully online or blended learning approach was adopted by all HEIs and, in response, a €15 million funding package was announced to help with online learning to assist students from lower incomes access laptops, tablets, and internet connectivity. The majority of this funding was earmarked for laptop purchases and with little detail on the connectivity issues that it may help address (Department of Education & Skills, 2020 ). Footnote 4

In addressing the specific responsibilities of HEIs in relation to quality assurance in a blended learning environment, Quality and Qualifications Ireland provide statutory quality guidelines (Quality & Qualifications Ireland, 2018 ). It is noteworthy that no mention of desirable broadband speeds for instructors or students is made in their report. However, under the heading of support available to students, it is recommended that “requirements for access, bandwidth and any prescribed hardware or software are appropriate and viable, are communicated well in advance to the learners and all requirements are published” (Quality & Qualifications Ireland, 2018 ). With regard to equality of opportunity, the guidelines also state that procedures in place include “teaching and learning resources for online learning which meet the provider’s specified expectations around equality of opportunity, interactivity and the empowerment of autonomous learning” (Quality & Qualifications Ireland, 2018 ). While such guidelines were written pre-COVID-19 when blended learning was ‘optional’, they have not been changed or updated at a time when many HEIs have pivoted towards a fully online or blended model of delivery and are highly relevant in the context of understanding access to high quality broadband in Ireland.

Broadband connectivity in Ireland

The digital divide between urban and rural areas in terms of access to high-speed broadband services in Ireland has long been recognised (Commission for Communications Regulation, 2009 ). Footnote 5 However, Ireland has generally lagged considerably behind its European peers in terms of implementing policy measures to address the issue (Palcic & Reeves, 2011 ). Ireland’s National Broadband Plan (NBP) was first published in August 2012 and set a target of a minimum download speed of 30Mbps for all households ahead of the EU’s target of 2020 for such speeds. However, a contract notice for the NBP was not issued until December 2015, with the signing of a contract with the preferred bidder delayed until November 2019 after a highly controversial procurement process.

The intervention area for the NBP includes rural areas that currently do not have access to high-speed broadband services and where commercial operators have no plans to deploy such services. Work commenced on the rollout of the NBP in 2020 and it is estimated that the plan will be fully delivered by 2026, with 40% of premises ‘passed’ by the end of the third year of deployment (Department of Communications, Climate Action and the Environment, 2019 ). As part of the plan, approximately 300 broadband connection points (WiFi hotspots) were to be deployed across the country by the end of 2020, so that those in rural communities can access high-speed broadband services in specific locations in advance of the full deployment of the NBP network.

To gain a better insight into the broadband services currently available across various platforms in Ireland, data provided by Commission for Communications Regulation ( 2020a ) shows that, at the end of September 2020, total broadband subscriptions stood at 1.83 million, with fixed broadband subscriptions accounting for 82.3% of this total and mobile broadband subscriptions accounting for the remainder. Footnote 6 However, the vast majority of higher speed FTTP (fibre to the premises), cable and VDSL (very-high-bit-rate digital subscriber line) fixed broadband services are only available in urban areas, with basic DSL (digital subscriber line), FWA (fixed wireless access), mobile broadband, and satellite services predominantly used in rural areas. Figure 1 shows the advertised headline speeds across each fixed broadband platform and provides some insight into the digital divide in terms of broadband performance between urban and rural areas. Basic DSL services have advertised download speeds of less than 30 Mbps, with over half of such connections in less than 10 Mbps range. Approximately 40% of FWA services have download speeds of less than 30 Mbps, while the vast majority of satellite services have download speeds of less than 30 Mbps. In contrast, VDSL, cable, and FTTP services offer download speeds well in excess of 30 Mbps.

Advertised broadband download speeds by fixed platform, Q3 2020. FWA fixed wireless access, FTTP fibre to the premises, DSL digital subscriber line, VDSL very-high-bit-rate digital subscriber line. Source ComReg ( 2020a )

In terms of mobile broadband services, the average download speeds for 3G and 4G mobile broadband connections are not available outside of cities and major transport routes. While the 3G and 4G coverage maps of each of the main mobile operators in Ireland show that the vast majority of the country is covered, the quality and stability of this coverage can vary widely, particularly in rural areas where coverage can be extremely poor. Various mobile consumer experience surveys conducted by the Commission for Communications Regulation have highlighted the fact that indoor mobile reception has a far higher incidence of experiencing service issues due to modern building materials (Commission for Communications Regulation, 2017a , 2019 ). This issue is amplified in rural areas where the Commission for Communications Regulation surveys reveal that rural mobile consumers experience the highest rates of service issues regardless of location within or outside the home (Commission for Communications Regulation, 2018 ). In recent years, the Commission for Communications Regulation have also expressed concern at the increase in the number of illegal roof aerials and mobile booster devices in rural areas, which can cause considerable interference to mobile phone spectrum and lead to significant deterioration in mobile reception in areas that already have limited coverage (Commission for Communications Regulation, 2017b ).

Outside of the high-speed broadband services provided by FTTP, cable, and most VDSL connections, it is subscribers to DSL, mobile, and FWA services that are more likely to have experienced connectivity issues during the lockdown caused by the pandemic. These issues were caused by higher data volumes on networks due to more people working from home, students at all levels engaging in online learning, and increased download activity in general as people accessed streaming video services or online gaming platforms (New York Times, 2020 ). Indeed, platforms such as Netflix, YouTube, and Disney were forced to temporarily throttle their video streams across Europe during March and April 2020, in order to limit bandwidth usage and ease pressure on congested networks (Financial Times, 2020 ).

In Ireland, a Commission for Communications Regulation survey in April 2020 found that over 60% of households increased their broadband usage during the lockdown, with 74% of households indicating that their home broadband connection was adequate for all work activities. However, this percentage fell to 67% for those living in rural areas, with households utilising either a DSL or mobile broadband connection also recording the lowest levels of satisfaction with the adequacy of their connection (Commission for Communications Regulation, 2020b ). More recent Commission for Communications Regulation survey data from June 2020 shows that household internet usage has increased further since April 2020, with those in rural areas continuing to have the lowest satisfaction level with the adequacy of their home broadband connection (Commission for Communications Regulation, 2020c ). The same survey also highlights how half of all participants indicated that they would be willing to spend more to get a better broadband service, showing the increased reliance on broadband for all households.

While it is very difficult to identify the exact download speed that a student would need to be able to fully engage with all aspects of online learning, particularly synchronous interactive video sessions, it is highly likely that students using basic broadband technologies will be affected most by connection issues. Such issues would be exacerbated if there are multiple internet users in the same household, as well as neighbouring households, where contention and congestion would severely impact available download speeds on technologies such as DSL. With continued uncertainty in relation to the potential for future surges in the pandemic, and the likelihood that many people will continue to have to work and learn remotely in the near future, those with poor fixed or mobile broadband services will continue to be at a major disadvantage relative to households with more stable higher speed services. This digital divide has the potential to create significant inequalities in education at all levels, particularly for students from lower socioeconomic backgrounds, who are more likely to experience issues in relation to access to, and affordability of, broadband and appropriate devices.

Materials and methods

The overarching goal of this paper is to assess access to high-speed broadband among college students in Ireland in the context of significantly increased levels of remote learning arising from the COVID-19 pandemic. It also seeks to examine disparities in access by geography, HEI and socioeconomic background. To do so, the analysis combines a variety of spatial data from four main sources.

First, this paper uses unique data on higher education student enrolments for 2017/18 from the Higher Education Authority (HEA). Footnote 7 This data defines an enrolment as a student registered in an Irish HEI and is based on a census of all enrolments undertaken in March 2018. For the academic year 2017/18 there were a total of 223,743 enrolments at Irish HEIs. Additional file 1 : Table A.1 presents a breakdown of enrolments by HEI), Footnote 8 though in this analysis data from TCD (16,755 enrolments) are excluded, due to non-reporting of relevant domicile data to the HEA, as are data from UCC (20,024 enrolments), due to likely misreporting of the same domicile data. Enrolments from outside Ireland are also excluded and, overall, this provides data on 167,576 enrolments for the 2017/18 academic year.

The HEA data includes information on student domicile, which is based on the address of permanent residence for 3 of the 5 years prior to initial enrolment. This information is available at electoral division (ED) level, small-scale geographic areas of which there are 3,409 in total. The population and geographic coverage of EDs vary considerably, with a mean overall population of 1,397 (range: 66 to 38,894) and a mean area of 19.6 km 2 (range: 0.01 to 125.94km 2 ). Thus, EDs provide a high level of spatial disaggregation in relation to student domicile. Finally, the HEA data also contains information on undergraduate (143,214; 85%) and postgraduate (24,362; 15%) enrolments, full-time (133,756; 80%) and part-time (33,820; 20%) enrolments, as well as the specific HEI each student attends.

The second dataset used is digital data on high-speed broadband coverage based on a mapping exercise undertaken by the Department of Communications, Climate Action and Environment (DCCAE). As discussed, the NBP is the Government’s plan to deliver high-speed broadband services to all premises in Ireland. As part of the NBP, a comprehensive analysis and mapping process of high-speed broadband availability across the country was undertaken, which involved the development of an interactive map which identifies geographic areas as being either served by the commercial sector or requiring State intervention under the NBP. Footnote 9

The DCCAE High-speed Broadband Map shows where high-speed broadband services are currently available and identifies locations and premises as being either ‘amber’, ‘blue’, or ‘light blue’. Amber areas are the target areas for the State intervention of the NBP i.e., areas in which there is currently no high-speed broadband. Blue areas are areas where commercial operators are delivering or have indicated plans to deliver high-speed broadband services, while light blue areas are areas where a commercial operator has committed to rollout high-speed broadband to 300,000 premises. Overall, of the 2,391,559 premises in the country as of Q3 2019, there were 537,595 (22.5%) premises in the intervention area (amber), 1,838,932 (76.9%) covered by commercial operators (blue), and 15,032 (0.6%) premises to be covered by planned commercial rural deployment.

The third dataset is the An Post GeoDirectory, a database of all residential and commercial buildings in Ireland, each matched to a unique postal address and geocoded to within a single square metre. A geocode is a code or reference number which pinpoints a specific location and the references or (X,Y) coordinates in GeoDirectory are GIS compatible. This means that the database can be easily mapped and analysed in conjunction with other spatial data e.g. attribute data on EDs or broadband coverage. In the analysis GeoDirectory data from 2018 is used.

Fourth, and finally, this paper also uses Census of Population-based data from 2016 to develop a profile of EDs with poor access to high-speed broadband. This includes an area-level deprivation index score (Haase & Pratschke, 2017 ), as well as ED-level small area population statistics (SAPS) data on median household income and PC ownership (CSO, 2020 ). The deprivation index is a measure of relative socioeconomic position based on the area of each home address. Scores range from -35.7 to + 16.8, with a mean of -5.0, and higher scores indicate greater affluence. Median household income is measured in Euros, while PC ownership is measured by the proportion of households in an ED with a computer.

To undertake the analysis and address the three RQs, GIS methods are used to (i) create ‘at-risk’ measures of poor access to high-speed broadband in the domicile areas of college students and, (ii) develop a socioeconomic profile of these at-risk areas. A GIS is an interactive computer program capable of assembling, storing, analysing, and displaying information that has been identified by location and, at its most basic level, a GIS application can be thought of as a computerised map. For example, to begin, the HEA enrolment data is first mapped at ED level. This allows the total number of college students with a domicile in each ED to be both stored and visually inspected, as well as the numbers of students attending each individual HEI with a domicile in that ED. For example, Fig. 2 shows a total of 39 students with a domicile in Monivea ED and the data disaggregates this total by HEI attended.

Source: Analysis of data from HEA, GeoDirectory, and National Broadband Plan

Geographic information systems approach.

At a more sophisticated level, a GIS can also help analyse multiple and complex layers of data, matching them to a specific point, locality, or area. The second step involves ‘overlaying’ Footnote 10 the DCCAE NBP map with the geocoded data on residential addresses from GeoDirectory, as illustrated in Fig. 2 . More specifically, using a ‘spatial join’, Footnote 11 it is possible to infer, for every residential address in Ireland, whether it has access to high-speed broadband. This is defined on the basis of whether the address is located in an amber or light blue area (i.e., no current high-speed broadband availability) or a blue area (i.e., currently has high-speed broadband availability)—Fig. 2 . It is then straightforward to calculate the proportion of residential addresses in each ED with high quality broadband access and, for different cut-offs or measures of areas at risk of poor access, to calculate the numbers of college students residing in these areas.

To do this, four measures or levels of at-risk areas are defined in terms of high-speed broadband coverage/availability. This approach follows that of Mohan et al. ( 2020 ), which in their study of second-level education in Ireland during COVID-19, created an indicator of ‘low broadband availability’ for secondary school catchment areas where high-speed broadband was available to fewer than 90% of residences, according to the NBP map. In defining at-risk areas in this paper, a lower set of thresholds are adopted, though the sensitivity of the results to less stringent assumptions are tested. In the main analysis, EDs are first classified at an overall level as having poor broadband coverage if high-speed broadband services are available at fewer than 50% of addresses. These EDs are then disaggregated into one of four mutually exclusive at-risk categories i.e. either (i) ‘low coverage’, where high-speed broadband is available at between 25 and 50% of residential addresses, (ii) ‘very low coverage’, where availability is between 10 and –25% of addresses, (iii) ‘minimal coverage’, where availability is between 0 and 10% of addresses, and (iv) ‘no coverage’, where broadband is available at 0% of residential addresses. This allows for the estimation of the numbers and proportions of college students living in areas with poor broadband coverage and who are therefore at risk of poor access to high quality internet connectivity according to a variety of at-risk measures (RQ1), and to consider differences in access by geography, HEI, and area-level characteristics (RQ2 and RQ3). In considering area-level characteristics, tests of statistical differences in means between EDs defined as having poor broadband and those defined as not are undertaken. To do so, standard two-sample t-tests assuming unequal variances are used.

To begin, Table 1 presents a breakdown of total enrolments according to the at-risk measures and helps answer RQ1 i.e. how many and what proportion of college students come from areas with poor broadband coverage and are therefore at risk of poor access to high-speed broadband? Overall, of the 167,576 students in the data, 16,462 (9.8%) had domiciles in EDs with low broadband coverage, 6008 (3.6%) in very low coverage EDs, 2801 (1.7%) in minimal coverage EDs, and 2,598 (1.6%) in EDs with no high-speed broadband coverage. Overall this implies that 16.6% of students (27,869) in the data were at risk of poor broadband access during the COVID-19 lockdown, assuming a 50% ED coverage threshold. Footnote 12 Table 2 presents a similar analysis for undergraduate and postgraduate students, as well as for full-time and part-time students. It shows that undergraduate and full-time students are more likely to face broadband access issues relative to postgraduate and part-time students respectively.

While Tables 1 and 2 give a good sense of the overall picture at a national level, it is also important to consider regional or geographical differences in access to high-speed broadband for college students – this addresses part of RQ2 i.e. are there significant differences in the proportions of college students from poor broadband coverage areas by geography and by HEI? To this end, Fig. 3 presents a breakdown by county of both the number and proportion of at-risk enrolments i.e. students with a domicile in EDs with below 50% coverage. It shows the highest absolute numbers of students without good broadband access are located mainly in the west and south-west of the country. In proportionate terms, however, it is counties in the north midlands and border area that are most disadvantaged in terms of access. The former result is in part driven by the high numbers of students from those counties, while the latter is a function of the generally poor broadband coverage in those areas. One caveat here is that, as mentioned, the data do not include enrolments at TCD or UCC, and this will likely distort the regional analysis to some extent. For example, the numbers of students in the south-west of the country are likely to be understated, given this is the catchment area of UCC. Nonetheless, they do suggest a strong regional dimension to broadband access issues.

Number and proportion of enrolments from at-risk electoral divisions by county.

As mentioned previously, there is extensive evidence of localised patterns of progression to HEIs in Ireland (Cullinan & Duggan, 2016 ; Cullinan & Halpin, 2017 ). In other words, students are much more likely to attend a HEI that is closer to their home than one farther away. Given this, along with the regional disparities illustrated in Fig. 2 , significant differences in domicile access to broadband by HEI might also be expected, as per RQ2. To investigate this, the proportions of enrolments at risk of poor broadband access by HEI were calculated. Figure 4 shows that, perhaps not unsurprisingly, there is considerable variation across HEIs in the measures that are used.

Proportion of enrolments from at-risk electoral divisions by HEI.

For example, starting with HEIs with good broadband access, 2.2% of students at IT Tallaght are from low coverage EDs, 0.5% from very low coverage EDs, 0.3% from minimal coverage EDs, and 0.3% from EDs with zero coverage. Thus, overall, only 3.3% of IT Tallaght students are classified as having poor access to high quality broadband as per the definition used here. However, at the other end of the spectrum, this is in stark contrast to St Angela’s College, where 19.2% of students are from low coverage EDs, 6.8% from very low coverage EDs, 4.0% from minimal coverage EDs, and 2.9% from EDs with zero coverage. Therefore, overall, 33.0% of students enrolled at St Angela’s College are classified as at risk.

The findings from the HEI analysis are consistent with the county-level analysis of enrolment numbers. The eight HEIs with the lowest proportion of at-risk students are located in Dublin, where broadband quality is high. HEIs with the highest proportions of at-risk students tend to be located in the west or midlands. Again, this is a function of the catchment areas of individual HEIs, as well as the unequal spatial distribution of high-quality broadband in Ireland. However, an important point to note here in the context of differences across HEIs is that broadband access is only one possible cause of the digital divide and some HEIs that have good access may face alternative challenges. For example, Fig. 4 also presents the proportion of socioeconomically disadvantaged students by HEI. Although not a primary focus of this paper, the data indicates that there is considerable variation in this measure across HEIs and this could be associated with other issues around digital learning resources for both students and HEIs e.g. affordability of, and access to, appropriate equipment, suitable home learning environments, and/or digital literacy skills.

The final piece of analysis involves considering differences in the socioeconomic profile of at-risk EDs in order to examine RQ3 i.e. are college students from lower socioeconomic backgrounds more likely to come from poor broadband coverage areas? In order to do this, this study examined data on area-level deprivation, median household income, as well as PC ownership by ED, and the results are presented in Table 3 . It also presents tests for statistical differences in the means of these variables between at-risk EDs and non-poor coverage EDs using two-sample t-tests. Overall, the results show that EDs with the poorest broadband coverage tend to be the most deprived and have the lowest median household income, though only the differences between no coverage EDs and non-poor coverage EDs are statistically significant. While there is little difference (either practically or statistically) in these socioeconomic measures overall for poor coverage areas when compared to non-poor coverage areas, it is notable that there is a gradient in both ED-level deprivation and median income as broadband coverage decreases. Thus, overall the evidence suggests that students from areas with the lowest levels of broadband coverage are more likely to be socioeconomically disadvantaged on average.

In addition to these two measures, data on PC ownership at ED level was also examined. Here statistically significant differences are found for very low, minimal, and no coverage EDs when compared to non-poor coverage EDs. Again, a gradient in PC ownership is evident, with lower ownership as broadband coverage decreases. This provides some evidence that students with poorer access to broadband services may also be disadvantaged in terms of access to computers for study purposes.

Discussion and implications

The persistent nature of the COVID-19 pandemic has forced many HEIs to move to mass online/blended learning. This raises concerns around differences in student access to digital learning resources while at home, including access to high-speed broadband services. This is because variation in the quality of broadband access may impact the type of online/blended model that staff can deliver and constrain how students can engage with online content. In this context, this study uses national data on higher education enrolments and broadband coverage to address three research questions relating to the numbers of students at risk of poor access to high-speed broadband and the variation in these numbers by geography, HEI, and socioeconomic background. Overall, the results suggest that almost 17% of higher education students in Ireland come from areas with poor broadband coverage, a figure that is consistent with the proportion of students that indicated access to reliable Wi-Fi was problematic in Spring 2020 (Union of Students in Ireland, 2020 ), as well as with data from the UK (Office for Students, 2020 ). (RQ1). They also show considerable variation by geography, as well as by HEI (RQ2). For example, more than a quarter of students in a number of HEIs come from areas with limited broadband coverage. Furthermore, the analysis presented shows that students facing the greatest constraints in terms of broadband coverage are more likely to be socioeconomically disadvantaged (RQ3).

An important implication of the findings is that some HEIs may have to significantly adjust their online delivery methods due to the considerable technological constraints that many students face. In addition, the findings also imply that different groups of students within each HEI may require different offerings, or have different capabilities to access blended/online content. As these constraints are largely based upon spatial factors, it is suggested that HEIs pay specific attention to the geographic pattern of their enrolments and consider tailoring their delivery or services to acknowledge these potential constraints. In this context, it may also be pertinent to ensure teaching staff attempt to gauge the connectivity of their students before deciding on a delivery strategy, if feasible.

On a related point, it should be noted that the analysis in this paper is based on data at ED level rather than at the specific household level, which would be preferable. Furthermore, a relatively conservative measure of which EDs have poor broadband coverage has been used and, as a result, the results may underestimate the true scale of at-risk students. Therefore, it is recommended that HEIs and government agencies use their more in-depth enrolment data to help more precisely identify individual at-risk students. Such an approach would allow HEIs to better develop policies and supports for students that face such connectivity issues. For example, HEIs could prioritise access to campus facilities for those from areas with poor coverage that are living at home to help ensure an effective and equal learning experience for all students. This could also possibly extend to offering subsidised on-campus accommodation for disconnected students from lower income backgrounds.

The issues raised in this study are not unique to Ireland, with problems relating to digital divides prevalent in the majority of developed and developing countries. This study points to potential connectivity issues for different groups of students in different HEIs. This may be an issue to varying degrees across different countries but is clearly worth examining since it highlights the need for HEIs to consider the geographic distribution of their students in designing appropriate policy and supports if moving towards mass online/blended delivery methods in response to COVID-19-related restrictions.

Limitations and future research directions

In terms of the analysis, a number of caveats should be borne in mind. First, in using the NBP intervention area mapping data, it is possible that some students in at-risk EDs have basic DSL or mobile broadband connections with download speeds that provide adequate support for most online learning applications. Nevertheless, survey data published by Commission for Communications Regulation ( 2020a , 2020b ) highlights that consumers with DSL or mobile broadband connections, as well as those in rural areas, had the lowest satisfaction rates in relation to the adequacy of their connection since the pandemic commenced. Second, data on in-home capacity issues such as WiFi quality or the number of people sharing home networks was not available. Such issues can impact the download speeds available within the home regardless of whether a household has access to a high-speed broadband technology or not. Third, it should be noted that there are some temporal differences across the four main data sources used in this paper. However, it is unlikely that the spatial distributions of the measures considered (i.e. higher education student domiciles, broadband availability, residential addresses, and census-based variables) will have changed considerably since the timing of their respective data. In addition, the underlying data is unlikely to have been directly affected by the pandemic. Finally, this paper has not considered whether some courses might require more bandwidth or lower latency broadband services than others, depending on the online learning applications being utilised. This is because course-level domicile data was not available.

With regard to future related research, it is suggested that HEIs or relevant teaching staff monitor the performance of students from areas of poor connectivity to evaluate variation in student engagement or performance relative to their better connected peers. For example, the results show that many ITs in Ireland have significant numbers of students with domiciles in poor broadband areas. Given that, relative to universities, progression beyond first year is a significant issue for ITs (McCoy & Byrne, 2017 ), this may be an issue that could be exacerbated by poor connectivity. Monitoring such issues could help inform the need for additional supports or services for these students.

Availability of data and materials

The data used in this paper are available on request from the Higher Education Authority in Ireland (student domicile data), the Department of the Environment, Climate and Communications (National Broadband Map), and An Post (GeoDirectory).

In the US, the Federal Communications Commission defines broadband as internet speeds of at least 25 megabits per second (Mbps) for downloads and 3 Mbps for uploads (Federal Communications Commission, 2018 ), while download speeds of less than 30 Mbps are defined as basic broadband in the EU.

It is also important to note that in the Irish National Digital Experience (INDEX) survey (National Forum for the Enhancement of Teaching and Learning in Higher Education, 2020 ), 77% of students sampled indicated they had access to reliable Wi-Fi. However, this survey was conducted in the autumn of 2019, prior to the pandemic, and focused on access on-campus, rather than off-campus. As a result, it is not of immediate relevance to the analysis in this paper.

Examples include Walsh et al. ( 2015 ), Cullinan and Duggan ( 2016 ), Cullinan and Halpin ( 2017 ), and Walsh et al. ( 2017 ).

This package also included the further education sector in Ireland, which is not within the focus of this paper.

Commission for Communications Regulation (ComReg) is the Irish communications regulator.

Figure B.1 in Additional file 1 : Appendix B provides a breakdown of fixed broadband subscription proportions by platform as of Q3 2020.

Spatially disaggregated enrolment data for the 2018/19 and 2019/20 academic years are not yet available.

This compares to a total of 228,503 enrolments in 2018/19 as per Sect. 3 .

The DCCAE NBP map was first issued in November 2014 and, following requests for supplementary information from operators, was re-issued in December 2015. In April 2017, DCCAE published an updated ‘High-speed Broadband Map’ that took account of commercial operator plans and new developments and, since then, the map is updated on a quarterly basis.

Overlay analysis is a GIS operation that integrates spatial data with attribute data by combining information from one GIS ‘layer’ (e.g. the NBP map) with another GIS layer (e.g. GeoDirectory addresses) to derive or infer an attribute for one of the layers.

A spatial join combines the attributes of two layers based on the spatial location of the features in the layers. It does so by appending the attributes of one layer to another within the GIS. It is possible to then use the additional information to perform analysis on the combined data.

This proportion increases to 22.4% (37,501 students) if a 60% coverage threshold is assumed, 30.9% (51,783) for a 70% threshold, 39.8% (66,618) for an 80% threshold, and 50.3% of students (84,306) assuming a 90% coverage threshold.

Bacher-Hicks, A., Goodman, J., & Mulhern, C. (2021). Inequality in household adaptation to schooling shocks: Covid-induced online learning engagement in real time. Journal of Public Economics, 193 , 104345

Article Google Scholar

Bothwell, E. (2020). UK universities favour blended learning approach for 2020–21. Times Higher Education. https://www.timeshighereducation.com/news/uk-universities-favour-blended-learning-approach-2020-21 . Accessed 28 July 2020.

Commission for Communications Regulation. (2009). Next generation broadband in Ireland: promoting the timely and efficient development of high-speed broadband services . ComReg Document 09/56. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2017a). Mobile consumer experience survey: Summer 2017 . ComReg Document 17/100a. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2017b). Consultation on permitting the general use of mobile phone repeaters. ComReg Document 17/103. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2018). Permitting the general use of mobile phone repeaters. Response to ComReg consultation document 17/103 and final decision . ComReg Document 18/58. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2019). Mobile consumer experience: Survey of consumers summer 2019 . ComReg Document 19/101. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2020a). Quarterly key data report. Data as of Q3 2020 . ComReg Document 20/119. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2020b). Impact of COVID-19 on home broadband use in Ireland . ComReg Document 20/35. Dublin: Commission for Communications Regulation.

Commission for Communications Regulation. (2020c). Impact of COVID-19 on consumer use and perception of telecommunication services . ComReg Document 20/61. Dublin: Commission for Communications Regulation.

Central Statistics Office (CSO). (2020). Census 2016 small area population statistics. https://www.cso.ie/en/census/census2016reports/census2016smallareapopulationstatistics/ . Accessed 28 July 2020.

Cullinan, J., & Duggan, J. (2016). A school-level gravity model of student migration flows to higher education institutions. Spatial Economic Analysis, 11 (3), 294–314

Cullinan, J. & Halpin, B. (2017). A Spatial Economic Perspective on Higher Education Choices. In J. Cullinan & D. Flannery (Eds.) Economic insights on higher education policy in Ireland: Evidence from a public system . London: Palgrave Macmillan.

Davies, A. (2020). Australian universities take tentative steps towards opening after COVID-19. https://www.theguardian.com/australia-news/2020/jun/12/australian-universities-take-tentative-steps-towards-opening-after-covid-19 . Accessed 15 July 2020.

Department of Communications, Climate Action and the Environment. (2019). Delivering the national broadband plan . Dublin: Department of Communications, Climate Action & Environment.

Department of Education and Skills. (2020). Minister Harris announces 17,000 laptops ordered to assist students with online and blended learning [Press release] 20 August. https://www.gov.ie/en/press-release/7143d-minister-harris-announces-17000-laptops-ordered-to-assist-students-with-online-and-blended-learning/ . Accessed 21 August 2020.

Dettling, L. J., Goodman, S., & Smith, J. (2018). Every little bit counts: the impact of high-speed internet on the transition to college. Review of Economics and Statistics, 100 (2), 260–273

Farrell, O., & Brunton, J. (2020). A balancing act: A Window into online student engagement experiences. International Journal of Educational Technology in Higher Education, 17 , 25

Federal Communications Commission. (2018). Broadband deployment plan [press release] 2 February. https://www.fcc.gov/reports-research/reports/broadband-progress-reports/2018-broadband-deployment-report . Accessed 29 July 2020.

Figlio, D., Rush, M., & Yin, L. (2013). Is it live or is it internet? Experimental estimates of the effects of online instruction on student learning. Journal of Labor Economics, 31 (4), 763–784

Financial Times. (2020). YouTube, Amazon and Netflix cut picture quality in Europe. https://www.ft.com/content/70333747-f180-4887-8a26-27ab6b230299 . Accessed 22 January 2021.

Flannery, D., & Cullinan, J. (2017). Economics and higher education policy. In: Economic insights on higher education policy in Ireland (pp. 3–24). Palgrave Macmillan.

Gaebel, M. (2020). COVID-19 and digitally enhanced learning and teaching: New opportunities in challenging times . European Universities Association. 28 May. https://www.eua.eu/resources/expert-voices/178-covid-19-and-digitally-enhanced-learning-and-teaching-new-opportunities-in-challenging-times.html . Accessed 28 July 2020.

Gonzales, A. L., McCrory Calarco, J., & Lynch, T. (2018). Technology problems and student achievement gaps: A validation and extension of the technology maintenance construct. Communication Research, 47 (5), 750–770

Haase, T., & Pratschke, J. (2017). The 2016 Pobal HP deprivation index. www.trutzhaase.eu . Accessed 14 June 2020.

Higher Education Authority. (2015). National plan for equity of access to higher education 2015–2019. Dublin: Higher Education Authority.

Higher Education Authority. (2019). A spatial and socio-economic profile of higher education institutes in Ireland. Dublin: Higher Education Authority.

Higher Education Authority. (2020). Key facts & figures: Higher education 2017/18 . Dublin: Higher Education Authority.

Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38 (5), 758–773

Kahu, E. R., Picton, C., & Nelson, K. (2020). Pathways to engagement: A longitudinal study of the first-year student experience in the educational interface. Higher Education, 79 , 657–673

Lamb, S., Maire, Q., Doecke, E., Macklin, S., Noble, K., & Pilcher, S. (2020). Impact of learning from home on educational outcomes for disadvantaged children . Victoria University.

Google Scholar

Lee, K. (2017). Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education, 33 , 15–23

McCoy, S., & Byrne, D. (2017). Student retention in higher education. In J. Cullinan & D. Flannery (Eds.), Economic insights on higher education policy in Ireland: Evidence from a public system. Palgrave Macmillan.

McGuire, P. (2020). Third-level teaching plans for Autumn, June 9th. https://www.irishtimes.com/news/education/third-level-teaching-plans-for-autumn-1.4269916 . Accessed 17 July 2020.

Mohan, G., McCoy, S., Carroll, E., Mihut, G., Lyons, S., & Mac Domhnaill, C. (2020). Learning for All? Second-level Education in Ireland During COVID-19 . ESRI Survey and Statistical Report Series No. 92. Dublin: Economic and Social Research Institute.

Montacute, R. & Holt-White, E. (2020). COVID-19 and social mobility impact brief: University access and finance . Sutton Trust Research Brief , Sutton Trust.

Naffi, N., Davidson, A.-L., Snyder, D. M., Kaufman, R., Clark, R. E., Patino, A., Gbetoglo, E., Duponsel, N., Savoie, C., Beatty, B., Wallace, G., Fournel, I., & Ruby (2020). Disruption in and by centres for teaching and learning during the COVID-19 pandemic. Quebec, Canada: International Observatory on the Societal Impacts of AI and Digital Technology. https://www.docdroid.com/L0khasC/whitepaper-disruption-in-and-by-centres-for-teaching-and-learning-during-the-covid-19-pandemic-leading-the-future-of-higher-ed-21-08-2020-pdf . Accessed 18 Apr 2021.

National Forum for the Enhancement of Teaching and Learning in Higher Education. (2020). INDEx finding from student and staff who teach in higher education . Dublin: National Forum for the Enhancement of Teaching and Learning in Higher Education.

New York Times. (2020). Surging traffic is slowing down our internet. https://www.nytimes.com/2020/03/26/business/coronavirus-internet-traffic-speed.html . Accessed 22 January 2021.

Office for Students. (2020). Digital poverty’ risks leaving students behind [Press release] 3 September. https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/digital-poverty-risks-leaving-students-behind/ . Accessed 16 January 2021.

Ortagus, J. C. (2017). From the periphery to prominence: An examination of the changing profile of online students in American higher education. The Internet and Higher Education, 32 , 47–57

Palcic, D., & Reeves, E. (2011). Privatisation in Ireland: Lessons from a European economy . Palgrave Macmillan.

Book Google Scholar

Panigrahi, R., Srivastava, P., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—a review of literature. International Journal of Information Management, 43 , 1–14

Paulsen, J., & McCormick, A.C. (2020). Reassessing disparities in online learner student engagement in higher education. Educational Researcher , published online 13 January 2020. https://doi.org/ https://doi.org/10.3102/0013189X19898690 .

Quality and Qualifications Ireland. (2018). Statutory quality assurance guidelines for providers of blended learning programmes . Dublin: Quality and Qualifications Ireland.

Raes, A., Detienne, L., Windey, I., & Depaepe, F. (2019). A systematic literature review on synchronous hybrid learning: Gaps identified. Learning Environments Research , published online 28 November 2019. https://doi.org/ https://doi.org/10.1007/s10984-019-09303-z .

Rasheed, A. R., Kamsin, A., & Nor, A. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144 , 103701

Sanchis-Guarner, R. Montalban, J., & Weinhardt, F. (2021). Home broadband and human capital formation. CESIFO Working Papers , 8846, January 2021. SSRN: https://ssrn.com/abstract=3772087 .

Silva, S., Badasyan, N., & Busby, M. (2018). Diversity and digital divide: Using the national broadband map to identify the non-adopters of broadband. Telecommunications Policy, 42 (5), 361–373

Skinner, B. T. (2019). Making the connection: Broadband access and online course enrolment at public open admissions institutions. Research in Higher Education, 60 (7), 960–999

Staff, C. (2020). Here's a list of colleges' plans for reopening in the fall. Chronicle of higher education. https://www.chronicle.com/article/Here-sa-List-of-Colleges-/248626 . Accessed 28 July 2020.

Stich, A. E., & Reeves, T. D. (2017). Massive open online courses and underserved students in the United States. The Internet and Higher Education, 32 , 58–71

Union of Students in Ireland. (2020). National report on students and COVID-19 . Dublin: Union of Students in Ireland. https://usi.ie/wp-content/uploads/2020/07/COVID_RESEARCH_FINAL.pdf . Accessed 28 July 2020.

Walsh, S., Cullinan, J., & Flannery, D. (2017). The impact of proposed higher education reforms on geographic accessibility to universities in Ireland. Applied Spatial Analysis and Policy, 10 , 515–536

Walsh, S., Flannery, D., & Cullinan, J. (2015). Geographic accessibility to higher education on the Island of Ireland. Irish Educational Studies, 34 (1), 5–23

Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37 , 46–57

Xu, D. & Jaggars, S.S. (2013b). Adaptability to online learning: Differences across types of students and academic subject areas. Community College Research Center Working Paper No. 54 , Columbia University, New York.

Zydney, J. M., McKimmy, P., Lindberg, R., & Schmit, M. (2019). Here or there instruction: Lessons learned in implementing innovative approaches to blended synchronous learning. TechTrends, 63 (2), 123–132

Download references

Acknowledgements

The authors wish to thank Victor Pigott from the Higher Education Authority for providing the student domicile data.

Not applicable, there is no funding associated with this article.

Author information

Authors and affiliations.

JE Cairnes School of Business & Economics, National University of Ireland Galway, Galway, Ireland

John Cullinan & Jason Harold

Department of Economics, Kemmy Business School, University of Limerick, Limerick, Ireland

Darragh Flannery & Dónal Palcic

Dublin and Department of Economics, Economic and Social Research Institute, Trinity College Dublin, Dublin, Ireland

You can also search for this author in PubMed Google Scholar

Contributions

JC, DF, and DP conceived the study. JC undertook the analysis and led the drafting of the manuscript. DF, JH, SL, and DP all helped source the data and provided substantial contributions to the design and reporting of the work. All authors read, edited, and approved the final manuscript and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to John Cullinan .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Additional HEI and enrolment information.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Cullinan, J., Flannery, D., Harold, J. et al. The disconnected: COVID-19 and disparities in access to quality broadband for higher education students. Int J Educ Technol High Educ 18 , 26 (2021). https://doi.org/10.1186/s41239-021-00262-1

Download citation

Received : 17 October 2020

Accepted : 13 April 2021

Published : 21 May 2021

DOI : https://doi.org/10.1186/s41239-021-00262-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Online learning
Blended delivery
Connectivity

Accessibility Links

Skip to content
Skip to search IOPscience
Skip to Journals list
Accessibility help
Accessibility Help

Click here to close this panel.

Purpose-led Publishing is a coalition of three not-for-profit publishers in the field of physical sciences: AIP Publishing, the American Physical Society and IOP Publishing.

Together, as publishers that will always put purpose above profit, we have defined a set of industry standards that underpin high-quality, ethical scholarly communications.

We are proudly declaring that science is our only shareholder.

Effect of Internet on Student's Academic Performance and Social Life

E S Soegoto 1 and S Tjokroadiponto 2

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 407 , International Conference on Informatics, Engineering, Science and Technology (INCITEST) 9 May 2018, Bandung, Indonesia Citation E S Soegoto and S Tjokroadiponto 2018 IOP Conf. Ser.: Mater. Sci. Eng. 407 012176 DOI 10.1088/1757-899X/407/1/012176

Article metrics

20079 Total downloads

Share this article

Author e-mails.

[email protected]

Author affiliations

1 Departement Management, Universitas Komputer Indonesia, Indonesia

2 Departement Teknik Dan Ilmu Komputer, Universitas Komputer Indonesia, Indonesia

Buy this article in print

The use of the internet has a huge impact on student achievement. This study was conducted to determine the effect of internet use on academic achievement, social life, and student activities in Bandung. This research will be very helpful for students, researchers, and curriculum developers to know the relationship of internet usage and academic achievement. This research was made by collecting the respondents from 2 Universities in Bandung, University Computer Indonesia and Institute Harapan Bangsa Technology. Respondents were randomly selected by 50 respondents. The results of this study can prove that the students' social life is influenced by the internet. Graphical representation of internet usage and its impact on students' social life shows that the use of the internet is very high, will minimize student social activity. This study shows that the use of the Internet for study purposes and academic achievement is directly proportional to each other while inversely proportional to student social life.

Export citation and abstract BibTeX RIS

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Front Psychol

A basic need theory approach to problematic Internet use and the mediating effect of psychological distress

Ting yat wong.

1 Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

2 Department of Counselling and Psychology, Hong Kong Shue Yan University, Hong Kong, China

Kenneth S. L. Yuen

3 Focus Program Translational Neuroscience, Neuroimaging Center, Johannes Gutenberg University Medical Center, Mainz, Germany

The Internet provides an easily accessible way to meet certain needs. Over-reliance on it leads to problematic use, which studies show can be predicted by psychological distress. Self-determination theory proposes that we all have the basic need for autonomy, competency, and relatedness. This has been shown to explain the motivations behind problematic Internet use. This study hypothesizes that individuals who are psychologically disturbed because their basic needs are not being met are more vulnerable to becoming reliant on the Internet when they seek such needs satisfaction from online activities, and tests a model in which basic needs predict problematic Internet use, fully mediated by psychological distress. Problematic Internet use, psychological distress, and basic needs satisfaction were psychometrically measured in a sample of 229 Hong Kong University students and structural equation modeling was used to test the hypothesized model. All indices showed the model has a good fit. Further, statistical testing supported a mediation effect for psychological distress between needs satisfaction and problematic Internet use. The results extend our understanding of the development and prevention of problematic Internet use based on the framework of self-determination theory. Psychological distress could be used as an early predictor, while preventing and treating problematic Internet use should emphasize the fulfillment of unmet needs.

INTRODUCTION

The development of the Internet has revolutionized the spread of information across the globe. Although the physical act of browsing the Internet could be largely solitary, the highly interactive nature of the Internet has created a virtual world-wide community. In this community people engage in a wide variety of online activities ranging from the exchange of information or ideas, socializing, and gaming, to more controversial activities like erotic interactions, gambling, and so on. The ease of accessibility of the Internet and the vast amount of activities have engaged a huge population, making the Internet a highly reinforcing and potentially addictive medium. The emergence of smartphones and tablet computers has further complicated the scenario. The round-the-clock availability of these mobile devices has blurred the boundary of Internet usage and it can be difficult for users to define when they are “logged in” to the Internet ( Wallace, 2014 ). Mobile devices with Internet connectivity have become part of modern life ( Hinić, 2011 ) and resulted in a large increase in online activities. Problematic Internet use has a potentially huge impact on individuals’ well-being ( Young, 1998a ). Problematic Internet use can create social disturbances as well as clinical issues, leading problematic users to experience academic, relational, financial, and occupational impairment as well as physical health problems in daily life. Despite all these consequences, whether or not problematic Internet use should be classified as a unidimensional psychiatric diagnosis is still being debated, given that Internet use is currently an umbrella term encapsulating a wide range of heterogeneous behaviors. So far, the only related disorder included by the American Psychiatric Association (APA) in the Diagnostic and Statistical Manual (DSM-V) is Internet gaming disorder, which has been identified as a potential pathological condition (that is, a condition requiring further study). Other pathological uses of the Internet fall into various diagnostic categories, such as sexual dysfunction and gambling disorder, based on the nature of the resulting behavior. Nonetheless, there seem to be generic factors underlying the diversity of problematic Internet behaviors. Laconi et al. (2014) review 45 assessment tools relating to problematic Internet use or addiction. Despite discrepancies in the definition and factor structures of these tools, two common factors emerged, namely negative outcomes and compulsive use (see also Wallace, 2014 ). These two factors align closely with recent neurological studies showing that excessive Internet use shares similar neurobiological mechanisms with substance and behavioral addiction ( Yuan et al., 2011 ), which might in turn be related to dysfunctional impulse control ( Shapira et al., 2000 ; Cao et al., 2007 ).

A more generic approach to understanding problematic Internet use across a wide range of online activities is to study the underlying motivations of users. Several researchers suggest that problematic Internet use stems from unmet real-life needs and that it is a way to relieve the problems encountered in daily life ( Young, 1998a ; Chak and Leung, 2004 ; Song et al., 2004 ). The Internet offers a highly accessible and immediate medium for users to satisfy such unmet needs ( Wan and Chiou, 2006a ). With reference to self-determination theory, needs can be grouped into three major types; relatedness, competence, and autonomy ( Deci and Ryan, 2000 ; Ryan and Deci, 2000 ). Relatedness refers to a desirable attachment to others involving love and care; competence denotes satisfying a sense of mastery, and autonomy refers to gaining satisfaction from one’s own choices and decisions and their enactment in a way that is coherent with one’s integrated sense of self. All three types of needs have been shown to be instrumental in explaining the motivational components of individual behaviors in educational, healthcare, psychotherapeutic, and sporting settings. They are also instrumental in explaining Internet usage ( Chen and Jang, 2010 ; Barnes and Pressey, 2011 ; Zhao et al., 2011 ; Wang, 2014 ). For instance, engagement in online social networks such as Facebook and Twitter can provide individuals with a sense of relatedness and autonomy by enabling them to connect with other people in a controlled manner ( Wan and Chiou, 2006a ; Sheldon et al., 2011 ; Nadkarni and Hofmann, 2012 ; Seidman, 2012 ). Participating in online gaming, on the other hand, could satisfy all three types of need via an intense interaction with other players (relatedness) to completing quests and achieving levels (competence), all through one’s own choices and decisions (autonomy; Kandell, 1998 ; Yee, 2006 ; Hsu et al., 2009 ). In general, the Internet has provided users with an immediate and easily accessible means to seek satisfaction and empowered them to control the way they present themselves regardless of their true identity or physical characteristics. It has thus become a very reinforcing stimulus.

The relationship between the satisfaction of needs and problematic Internet use can be further corroborated by the presence of psychological distress. In this context, psychological distress can be defined as general emotional disturbance related to negative mood, anxiety, and stress that most individuals will experience across their lifespan. Epidemiological studies show high levels of comorbidity between problematic Internet use and mood/anxiety disorders ( Shaw and Black, 2006 ), and a positive correlation between psychological distress and severity of problematic Internet use ( Young and Rogers, 1998 ; Caplan, 2002 ; Chak and Leung, 2004 ; Yuen and Lavin, 2004 ; Ebeling-Witte et al., 2007 ; Ceyhan and Ceyhan, 2008 ; Yeh et al., 2008 ). Davis’s (2001) cognitive behavioral model proposes that psychological distress, such as depression and anxiety, is an essential and significant catalyst of problematic Internet use. The psychological distress either develops from Internet use or exists long before this behavior is established, and can result from unmet needs ( Ryan and Deci, 2000 ). For example, psychological distress can be associated with needs which are unmet as a result of shyness-induced social difficulties ( Chak and Leung, 2004 ; Yuen and Lavin, 2004 ; Ebeling-Witte et al., 2007 ). Online communication provides shy individuals with a safety zone that enables them to avoid face-to-face interpersonal communication, freeing them from the negative and undesirable feelings associated with it. Individuals with poor oﬄine social relationships seek compensation from online interactions ( Bessière et al., 2008 ). These individuals therefore may come to depend on the Internet to connect with others in order to satisfy their need for relatedness, potentially leading to excessive use ( Chak and Leung, 2004 ; Ebeling-Witte et al., 2007 ).

This study examines generic motivation factors leading to problematic Internet use. Generic motivation factors are used since the Internet has become an everyday tool for satisfying a wide range of human needs, and self-determination theory has also been shown to be useful in explaining Internet behaviors. It remains unclear how well self-determination theory explains the motivational components of problematic Internet use, and whether psychological distress acts as an essential catalyst in its development. We hypothesize that psychological distress acts as a mediator in developing behavioral patterns of excessive Internet use. On this basis, we propose a theoretical model in which problematic Internet use is fully mediated by psychological distress and originates from unfulfilled basic psychological needs (see Figure Figure1 1 ) while the direct path from basic needs to problematic Internet use is expected to be non-significant. We recruited University students to test our model because they are among the heaviest users of the Internet and we expected a relatively high proportion of problematic users. In addition, the identity change which one undergoes in early adulthood poses a significant risk for psychological distress. Students are accordingly a suitable population on which to test our theoretical model ( Morahan-Martin and Schumacher, 2000 ; Widyanto and Griffiths, 2006 ; Yen et al., 2009 ).

An external file that holds a picture, illustration, etc.
Object name is fpsyg-05-01562-g001.jpg

The hypothesized model with the effect of need satisfaction to problematic Internet use mediated by psychological distress .

MATERIALS AND METHODS

Participants.

A total of 250 questionnaires were distributed to undergraduates in a University in Hong Kong, of which 229 valid responses were returned. This was a cross-sectional design aiming at studying the problematic Internet use among young adults. The age of the participants ranged from 19 to 25 ( M = 21.30, SD = 1.47). Of these, 72 were male, with a mean age of 21.35 (SD = 1.48) and 157 were female, with a mean age of 21.27 (SD = 1.47). The ethical standards of this study were approved by the Research Subcommittee of the Department of Counselling and Psychology, Hong Kong Shue Yan University. All participants were bilingual in English and Chinese, and they reported no problem in comprehending the questions.

The questionnaire consisted of a battery of measures of problematic Internet use, needs satisfaction, and psychological distress. The original English versions were used to avoid inconsistent factor structures induced by the translation processes. Furthermore, the participants were all proficient in English and during the data collection process the experimenter received no requests for clarifications of the questionnaire items.

Problematic Internet use

This was measured using the Internet Addiction Test ( Young, 1998b ). Young (1998a) suggests that problematic Internet use shares characteristics with pathological gambling and firstly proposed the latter as the basis of assessment criteria. To enable a more accurate assessment, the Internet Addiction Test was then developed, including 20 items derived from the original diagnostic questionnaire ( Young, 1998b ). Factor analysis shows six factors related to excessive Internet use; salience, excessive use, neglect of work, anticipation, low control, and neglect of social life. Among all the available tools, the Internet Addiction Test is used most frequently ( Laconi et al., 2014 ). It has been shown to have a stable factor structure in a range of studies ( Chang and Law, 2008 ; Faraci et al., 2013 ) as well as satisfactory test–rest reliability (0.73–0.88) and good to excellent concurrent validity (0.46–0.90; Laconi et al., 2014 ). The Cronbach’s α ranges from 0.52 to 0.82 ( Widyanto and McMurran, 2004 ), and for this study was 0.865 (subscales α = 0.48 to 0.74, except for the anticipation subscale, α = –0.06, probably due to this subscale consisting of only two items).

Psychological distress

Psychological distress was measured using the 21-item short version of the depression anxiety stress scales (DASS-21; Henry and Crawford, 2005 ). This comprises three 7-item subscales covering depression, anxiety, and stress. The DASS-21 has been shown to be reliable and has been validated in a non-clinical population. The Cronbach’s α of the subscales in this study were 0.806 (depression), 0.778 (anxiety), and 0.750 (stress).

Needs satisfaction

The general version of the basic psychological need satisfaction (BPNS) was used ( Sheldon et al., 2001 ). It consists of a total of 21 items with three subscales; autonomy, competence, and relatedness. The Cronbach’s α values in this study were 0.467 (autonomy), 0.659 (competence), and 0.696 (relatedness).

DATA ANALYSIS

A two-stage structural equation modeling (SEM) approach was used to evaluate the model ( Anderson and Gerbing, 1988 ). A confirmatory factor analysis (CFA) was first conducted to examine whether or not the measurement model could provide an acceptable fit to the instruments used. This procedure confirmed the latent factor structure as intended to be measured by the assessment tools. The structural model for the hypothesized paths between the latent variables was then tested. For mediation analysis, the Sobel (1982) test and Tofighi and MacKinnon’s (2011) criteria were used to evaluate the effect. To further test the validity of the structural model, alternative models with different influence paths were tested and compared using the fit indices as well as the Akaike information criterion (AIC) and Bayesian information criterion (BIC). A combination of four indices was used to evaluate model fitness: chi-square, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMSR), and comparative fit index (CFI). A non-significant chi-square value suggests the specified model is congruent with the observed data and is a reasonable measure of fit ( Barrett, 2007 ). The RMSEA is considered to be adequate when it falls below 0.10. The SRMSR measures the standardized difference between the observed and predicted correlation. It is considered acceptable at values at 0.08 or less ( Hu and Bentler, 1999 ). The CFI considers the number of parameters, or paths, in the model and is considered to be good at 0.90 or above. Using these indices as references, sample size estimation was performed using the method developed by Westland (2010) . With a small anticipated effect size of 0.1, a conventional desired power of 0.8, the estimated smallest sample size required to detect an effect was n = 197. All SEM testing was performed with AMOS 20.0 and the other statistical analyses were conducted using SPSS.

DESCRIPTIVE STATISTICS

Mean, standard deviation, and correlation among the observed variables are shown in Table Table1 1 . Independent t -tests were carried out to examine any gender differences. There was no significant difference in terms of age or DASS-21 and BPNS scores (see Table Table2 2 ), but male participants scored higher on the Internet Addiction Test ( t 227 = 2.27, p = 0.02, d = 0.30) which is consistent with previous studies (see for example Chak and Leung, 2004 ; Chou et al., 2005 ).

Mean, standard deviation, and correlation among the observed and latent variables.

Descriptive statistics and pair-wise comparisons between the two gender groups.

Participants in this study had moderate Internet Addiction Test scores ( M = 49.97) and long Internet use times ( M = 25.26 h per week), as compared with data from previous population surveys or normative data. The Internet Addiction Test mean scores of both male and female respondents fell within the frequent (40–69) range of problematic Internet use ( Young, 1998a ; Laconi et al., 2014 ). Although this range categorization is arbitrary, the average hours of Internet use per week of these participants (males 30.49 h; females 22.86 h) fell into the at-risk range as determined by the criteria developed in one population survey in Shanghai ( Xu et al., 2012 ). With reference to data from a recent population study of 12,446 high school students in nearby Guangdong Province, who shared a common language and cultural characteristics with the sample in this study, these participants’ amount of Internet use placed them above the 67th percentile in terms of normative Internet usage ( Wang et al., 2011 ). These findings provide converging evidence of frequent Internet usage among our current sample. Although there is yet a consensual cutoff score defining problematic Internet use, the descriptive statistics imply a large part of the study population exhibits at least some degree of problematic use.

MODEL TESTING

The chi-square of the measurement model was 104.03 ( df = 45, n = 229), with a p < 0.01. This initial result demonstrated that it was a moderately good fit, with RMSEA = 0.09 < 0.10, SRMR = 0.06 < 0.08, and CFI = 0.95 > 0.90. All the factor loadings of the different latent variables were significant. A detailed measurement model is shown in Figure Figure2 2 . This result suggested that the model fit was acceptable and further evaluation would be worthwhile.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-05-01562-g002.jpg

The measurement model among need satisfaction, problematic Internet use and psychological distress. ***Path coefficients are significant at a 0.001 level, p < 0.001.

The maximum likelihood method was used to explore the fitness of the structural model (see Figure Figure3 3 ). The result showed that the model has a relatively good fit, with chi-square (45, n = 229) = 104.03 at p < 0.001, RMSEA = 0.08 < 0.10, SRMR = 0.06 < 0.08, CFI = 0.95 > 0.90, AIC = 170.03, and BIC = 283.35. The model accounted for 27% of the variance in problematic Internet use. A power calculation using the method developed by MacCallum et al. (1996) revealed satisfactory power at π = 0.71. As predicted, the only significant path to problematic Internet use from needs satisfaction was mediated by psychological distress. All the structural paths were significant at the 0.001 level, except for the path leading from the satisfaction of basic needs to problematic Internet use.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-05-01562-g003.jpg

The structural model of the influence of need satisfaction to problematic Internet use with mediating effect of psychological distress. ***Path coefficients are significant at a 0.001 level, p < 0.001.

To statistically examine the proposed mediating effect, the Sobel (1982) test was used. The result supported the mediating effect of psychological distress between needs satisfaction and problematic Internet use ( Z = 2.41, p = 0.02 < 0.05). A further test using Tofighi and MacKinnon’s (2011) criteria confirmed the existence of a significant mediating effect (mean mediation term = –0.44, 95% C.I. = –0.24 to –0.68).

ALTERNATIVE MODELS

Three alternatives were tested to further confirm our theoretical model ( Table Table3 3 ). The first was a model in which psychological needs satisfaction and psychological distress both contributed to a direct path to problematic Internet use, with the mediating path from needs satisfaction to psychological distress omitted. Compared to the hypothesized theoretical model with the mediating path, this model has a poorer fit (model A1). All the fit indices and AIC/BIC suggested that the hypothesized model had a better fit, providing support for the theoretical model.

Summary of model fit indices and mediation tests of the theoretical model and the three alternative models (A1: without a mediating path from need satisfaction to psychological distress; A2: removed subscales with low reliability, autonomy in BPNS and anticipation in Internet Addiction Test; A3: treating both BPNS and Internet Addiction Test as unitary constructs).

Despite the theoretical model confirming the validity of the latent variables, low levels of internal consistency were observed in the anticipation subscale of the Internet Addiction Test and the autonomy subscale of the BPNS. Both subscales were used in this analysis because studies have demonstrated their utility across different countries. For instance, autonomy is suggested to be a universal need ( Ryan and Deci, 2003 ; Vansteenkiste et al., 2006 ; Rudy et al., 2007 ; Chirkov, 2009 ). While the anticipation subscale only consists of two items and, hence, is intrinsically prone to generating unstable statistics, the autonomy subscale could be attributed to a different cultural perception of the constructs among Hong Kong adolescents ( Iyengar and Lepper, 1999 ; Chang and Law, 2008 ; Markus and Schwartz, 2010 ). For instance, it has been reported that Hong Kong young people expect to gain autonomy at a significantly later age than their western counterparts ( Feldman and Rosenthal, 1991 ). Compared to the western population, the developmental delay in terms of Hong Kong students’ expectation for autonomy may correspondingly influence their needs. The higher variance in their need for autonomy may thus lead to a lower internal consistency for this subscale. To rule out the potential confounding effect due to the low internal consistency of these two subscales, two additional alternative models were tested ( Table Table3 3 ); one with these two subscales removed altogether (model A2) and the other treating both BPNS and Internet Addiction Test as measures of unitary latent constructs (model A3). If the low internal consistency of these two subscales was genuinely a threat to the validity of the original hypothesized model, removing them (model A2) should significantly change the path coefficient. On the other hand, collapsing all the subscales into one latent variable (model A3) should enhance internal consistency. If the alternative model A3 revealed significantly different path coefficients, this would indicate that the effect of the autonomy and anticipation subscales in the original model could not be relied upon. The results showed that both alternatives demonstrated comparable fitness when compared to the hypothesized structural model, and the mediation effect of psychological distress remained significant ( Table Table3 3 ). This shows that the internal consistency does not pose a threat to the hypothesized model.

DEGREE OF PROBLEMATIC INTERNET USE

As mentioned above, the Internet Addiction Test scores of these participants were in the moderate range. It is also of interest to explore whether the hypothesized model holds for users demonstrating different levels of problematic Internet use. Thus, the sample was median split into two groups (light users vs. heavy users) according to their Internet Addiction Test scores ( M = 49.97). Table Table4 4 summarizes their differences in terms of the variables of interest. Participants in the heavy user group consistently spent more time using the Internet compared to the light user group ( t 227 = 4.94, p < 0.01, d = 0.65). They also suffered from poorer psychological health (depression t 227 = –5.16, p < 0.01, d = 0.68; anxiety t 227 = 3.19, p < 0.01, d = 0.35; and stress t 227 = 3.67, p < 0.01; d = 0.48) and scored significantly lower in satisfaction of two of the basic needs, namely autonomy ( t 227 = 2.54, p < 0.01, d = 0.34) and competence ( t 227 = 2.63, p = 0.01, d = 0.35).

Descriptive statistics and pair-wise comparisons between the two problematic Internet use groups.

The possibility of whether the hypothesized model fits both groups equally well was explored by testing a multigroup nested model. Table Table5 5 summarizes the overall model fitness and path coefficients between the three latent variables for both groups. Consistent with the hypothesized model, the indirect path between basic needs and problematic Internet use was significantly mediated by psychological distress in the heavy users group only. In contrast, the path coefficients pattern was different for the light users group. Psychological distress still significantly predicted problematic Internet use and the direct path between basic needs and problematic Internet use was marginally significant ( p = 0.050). There was no significant mediation effect.

Goodness-of-fit indices and path coefficients of the multi-group nested model.

This study addressed data from University students who used the Internet frequently and likely suffered some degree of problematic Internet use. SEM was applied in order to examine the interplay between psychological needs satisfaction, psychological distress, and problematic Internet use the results support the proposition that self-determination theory is instrumental in explaining the motivational components of problematic Internet use, and their relationship is mediated by psychological distress. Needs satisfaction was significantly and negatively associated with psychological distress, which itself was significantly and positively associated with problematic Internet use. This conforms to the hypothesized mediating role of psychological distress in problematic Internet use ( Young, 1998b ; Davis, 2001 ; Yeh et al., 2008 ; Wang et al., 2011 ).

Studies show that individuals will attempt to satisfy their needs through participating in different kinds of Internet activities ( Kandell, 1998 ; Wan and Chiou, 2006a , b ; Yee, 2006 ; Hsu et al., 2009 ; Back et al., 2010 ; Huang, 2010 ; Barnes and Pressey, 2011 ; Sheldon et al., 2011 ; Nadkarni and Hofmann, 2012 ; Seidman, 2012 ). The emergence of mobile technologies has broadened the functionality and utility of the Internet, thereby providing people with an easily accessible and immediate means to satisfy their basic needs. In this context, these findings extend our understanding of what motivates people to get involved in Internet activities in general. Based on the framework of self-determination theory, individuals look for ways to satisfy their basic needs, autonomy, competency, and relatedness online. If an individual fails to fulfill his or her basic needs, the level of psychological distress increases ( Ryan and Deci, 2000 ). Such individuals are more likely to turn to Internet activities for compensation ( Song et al., 2014 ) and may develop problematic use patterns. This is consistent with the comorbidity observed between mood/anxiety disorders and problematic Internet use in previous work ( Shaw and Black, 2006 ) and with a recent report by Dong et al. (2011) suggesting symptoms relating to psychological distress significantly predict problematic Internet usage.

Using the Internet, especially via mobile devices, can be an immediate way to satisfy basic needs. Individuals feeling distress are more likely to make use of this approach ( Song et al., 2014 ). Such individuals are not satisfied with conventional means and, as a result, Internet activities provide them an alternative way to gain satisfaction ( Yuen and Lavin, 2004 ). Some users, however, being poorly adapted to this alternative method, fail to derive satisfaction from Internet activities thus leaving their needs unresolved. For example some Internet users receiving high levels of online social support do not feel their emotional loneliness alleviated ( Hardie and Tee, 2007 ). In such circumstances, individuals’ unmet needs are unresolved and they may suffer from increased psychological distress which in turn motivates them to go online more often to satisfy their needs, creating a vicious cycle of Internet reliance. The development of reliance implies that the Internet is more than just a tool or a leisure activity, with users starting to develop compulsions as well as neglecting real-life issues. A number of studies show a significant association between problematic Internet use and escapism ( Yee, 2006 ; Kuss et al., 2012 ). Individuals may use the Internet to escape the cognitive and emotional challenges arising from unsatisfying life circumstances oﬄine ( Henning and Vorderer, 2001 ). This hinders them from finding other ways to resolve their unmet needs and blocks effective intervention, further promoting the development of problematic Internet use ( Ko et al., 2012 ).

The exploratory analysis and split-half model fitting presented here provide additional evidence for the proposed mechanism. Pairwise comparison indicated significant differences in basic needs satisfaction and psychological distress between the light and heavy user groups. The former demonstrated a marginally significant association between needs satisfaction and problematic Internet use. This supports the proposition that Internet users are motivated by an urge to fulfill their needs when they first engage in online activities. By contrast, the heavy user group showed a significant mediation effect for psychological distress, similar to that shown in the main analysis. Despite the reduced statistical power in the split-half analysis, the model was still a reasonable fit. Together with the observed differences in terms of psychological distress across the two groups, this exploratory analysis suggests that the amount of Internet use is associated with a reduction in basic needs satisfaction when usage is within the normal range (light users group). When Internet usage reaches a problematic level, this association is mediated by an increase in psychological distress.

An alternative explanation for our findings is that problematic Internet use significantly worsens psychological needs satisfaction. With the correlational nature of SEM, we could not rule out the possibility that problematic Internet use reciprocally influences psychological needs satisfaction. In fact, this reciprocal influence is a potential basis for the development of a vicious cycle. This alternative explanation should be further tested by studies adopting experimental manipulation on the amount of Internet use and examining psychological needs satisfaction as the outcome measure.

Psychological distress helps to identify individuals at risk of developing problematic Internet usage. Nevertheless, the results reported here suggest that measures for relieving psychological distress ( Caplan, 2002 ; The Center for Internet Addiction, 2009 ) may not be enough to prevent and treat Internet addiction. An additional and alternative measure to help clients escape from the grip of the Internet might actually be to help them meet their real-life basic needs. One specific example is facilitating the improvement of social skills by individuals with social difficulties. This may ease problematic Internet use among such persons by fulfilling their needs of competence and relatedness through participation in social groups ( Yeh et al., 2008 ). This will, in turn, allow these individuals to gain satisfaction from real-life situations.

LIMITATIONS

Problematic Internet use is examined as a generic and integrated behavior in the present study, since the main aim was to test the basic motivational underpinnings. Nevertheless, there are specific types of addictive online behaviors which have been shown to be associated with specific cognitive deficits, such as impulse control difficulties ( The Center for Internet Addiction, 2009 ). These findings do not exclude the role of such cognitive deficits in the development of specific addictive online behaviors. Future research could examine whether basic needs satisfaction and psychological distress could be incorporated in a model of these cognitive deficits to better predict the course of specific online addictive behaviors.

The assessment tools for problematic Internet use have been widely criticized due to the dynamic change of Internet environment. Though the Internet Addiction Test is the most widely used, its factor structure varies across studies. A similar issue with the internal consistency of subscales has been observed in this sample, although the potential confounding effect has been excluded by testing alternative models. In addition, some of the Internet Addiction Test items are now outdated (e.g., evaluating e-mail use but not instant messages; Laconi et al., 2014 ). Future studies should look at developing an updated assessment tool to better evaluate Internet use so as to facilitate research on the psychopathology of problematic use. A properly validated assessment tool would also help to identify clinical cases.

The sample used in this study, due to age and developmental stage, represents only a cross-sectional cohort of the population that presents with high risk of problematic Internet use. Most participants in this study were frequent users. The exploration of the split-half analysis was restricted by the reduced statistical power and the arbitrary segregation between light and heavy users. Accordingly, these findings are not generalizable to the general etiology of problematic Internet use and the use of SEM with cross-sectional data forbids examination of causality. A stratified, population-based study should be conducted to establish a generic etiology model for problematic Internet use. In addition, a longitudinal study would provide further empirical evidence about the causal relationship of the tested constructs.

Internet activities can provide a sense of satisfaction of basic psychological needs including competency, relatedness, and autonomy. Individuals who have developed psychological distress due to these unmet needs are particularly vulnerable to problematic Internet use. The compulsion to use the Internet also blocks individuals from looking for other way to resolve their distress, which in turn accelerates and intensifies the degree of problematic use. This study has extended self-determination theory to explain the motivational components of problematic Internet use, and provided empirical support for the mediating role of psychological distress between needs satisfaction and problematic Internet use. Effective interventions should therefore address the individual’s motivational needs as well as his or her psychological distress.

Conflict of Interest Statement

The Associate Editor Reinout W. Wiers declares that, despite being affiliated to the same institution as author Ting Yat Wong, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Anderson J., Gerbing D. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 411–423 10.1037/0033-2909.103.3.411 [ CrossRef ] [ Google Scholar ]
Back M. D., Stopfer J. M., Vazire S., Gaddis S., Schmukle S. C., Egloff B., et al. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychol. Sci. 21 372–374 10.1177/0956797609360756 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Barnes S. J., Pressey A. D. (2011). Who needs cyberspace? Examining drivers of needs in Second Life. Internet Res. 21 236–254 10.1108/10662241111139291 [ CrossRef ] [ Google Scholar ]
Barrett P. (2007). Structural equation modelling: adjudging model fit. Pers. Individ. Dif. 42 815–824 10.1016/j.paid.2006.09.018 [ CrossRef ] [ Google Scholar ]
Bessière K., Kiesler S., Kraut R. E., Boneva B. (2008). Effects of Internet use and social resources on changes in depression. Inform. Commun. Soc. 11 47–70 10.1080/13691180701858851 [ CrossRef ] [ Google Scholar ]
Cao F., Su L., Liu T., Gao X. (2007). The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. Eur. Psychiatry 22 466–471 10.1016/j.eurpsy.2007.05.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Caplan S. E. (2002). Problematic Internet use and psychosocial well-being: development of a theory-based cognitive-behavioral measurement instrument. Comput. Hum. Behav. 18 553–575 10.1016/S0747-5632(02)00004-3 [ CrossRef ] [ Google Scholar ]
Ceyhan A. A., Ceyhan E. (2008). Loneliness, depression, and computer self-efficacy as predictors of problematic internet use. Cyberpsychol. Behav. 11 699–701 10.1089/cpb.2007.0255 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Chak K., Leung L. (2004). Shyness and locus of control as predictors of Internet addiction and Internet use. Cyberpsychol. Behav. 7 559–570 10.1089/1094931042403073 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Chang M. K., Law S. P. M. (2008). Factor structure for young’s internet addiction test: a confirmatory study. Comput. Hum. Behav. 24 2597–2619 10.1016/j.chb.2008.03.001 [ CrossRef ] [ Google Scholar ]
Chen K. C., Jang S. J. (2010). Motivation in online learning: testing a model of self-determination theory. Comput. Hum. Behav. 26 741–752 10.1016/j.chb.2010.01.011 [ CrossRef ] [ Google Scholar ]
Chirkov V. I. (2009). A cross-cultural analysis of autonomy in education: a self-determination theory perspective. Theory Res. Educ. 7 253–262 10.1177/1477878509104330 [ CrossRef ] [ Google Scholar ]
Chou C., Condron L., Belland J. C. (2005). A review of the research on Internet addiction. Educ. Psychol. Rev. 17 363–388 10.1007/s10648-005-8138-1 [ CrossRef ] [ Google Scholar ]
Davis R. A. (2001). A cognitive-behavioral model of pathological Internet use. Comput. Hum. Behav. 17 187–195 10.1016/S0747-5632(00)00041-8 [ CrossRef ] [ Google Scholar ]
Deci E. L., Ryan R. M. (2000). The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol. Inq. 11 227–268 10.1207/S15327965PLI1104-01 [ CrossRef ] [ Google Scholar ]
Dong G., Lu Q., Zhou H., Zhao X. (2011). Precursor or sequela: pathological disorders in people with Internet addiction disorder. PLoS ONE 6 : e14703 10.1371/journal.pone.0014703 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ebeling-Witte S., Frank M. L., Lester D. (2007). Shyness, Internet use, and personality. Cyberpsychol. Behav. 10 713–716 10.1089/cpb.2007.9964 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Faraci P., Craparo G., Messina R., Severino S. (2013). Internet addiction test (IAT): which is the best factorial solution? J. Med. Internet Res. 15 : e225 10.2196/jmir.2935 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Feldman S. S., Rosenthal D. A. (1991). Age expectations of behavioral autonomy in Hong Kong, Australian and American youth: the influence of family variables and adolescent’s values. Int. J. Psychol. 26 1–23 10.1080/00207599108246846 [ CrossRef ] [ Google Scholar ]
Hardie E., Tee M. Y. (2007). Excessive Internet use: the role of personality, loneliness and social support networks in Internet addiction. Aust. J. Emerg. Technol. Soc. 5 34–47. [ Google Scholar ]
Henning B., Vorderer P. (2001). Psychological escapism: predicting the amount of television viewing by need for cognition. J. Commun. 51 100–120 10.1111/j.1460-2466.2001.tb02874.x [ CrossRef ] [ Google Scholar ]
Henry J. D., Crawford J. R. (2005). The short-form version of the depression anxiety stress scales (DASS-21): construct validity and normative data in a large non-clinical sample. Br. J. Clin. Psychol. 44 227–239 10.1348/014466505X29657 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hinić D. (2011). Problems with ‘Internet addiction’ diagnosis and classification. Psychiatr. Danub. 23 145–151. [ PubMed ] [ Google Scholar ]
Hsu S. H., Wen M.-H., Wu M.-C. (2009). Exploring user experiences as predictors of MMORPG addiction. Comput. Educ. 53 990–999 10.1016/j.compedu.2009.05.016 [ CrossRef ] [ Google Scholar ]
Hu L., Bentler P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6 1–55 10.1080/10705519909540118 [ CrossRef ] [ Google Scholar ]
Huang C. (2010). Internet use and psychological well-being: a meta-analysis. Cyberpsychol. Behav. Soc. Netw. 13 241–249 10.1089/cyber.2009.0217 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Iyengar S. S., Lepper M. R. (1999). Rethinking the value of choice: a cultural perspective on intrinsic motivation. J. Pers. Soc. Psychol. 76 349–366 10.1037/0022-3514.76.3.349 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kandell J. J. (1998). Internet addiction on campus: the vulnerability of college students. Cyberpsychol. Behav. 1 11–17 10.1089/cpb.1998.1.11 [ CrossRef ] [ Google Scholar ]
Ko C., Yen J., Yen C., Chen C., Chen C. (2012). The association between Internet addiction and psychiatric disorder: a review of the literature. Eur. Psychiatry 27 1–8 10.1016/j.eurpsy.2010.04.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kuss D. J., Louws J., Wiers R. W. (2012). Online gaming addiction? Motives predict addictive play behavior in massively multiplayer online role-playing games. Cyberpsychol. Behav. Soc. Netw. 15 480–485 10.1089/cyber.2012.0034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Laconi S., Rodgers R. F., Chabrol H. (2014). The measurement of Internet addiction: a critical review of existing scales and their psychometric properties. Comput. Hum. Behav. 41 190–202 10.1016/j.chb.2014.09.026 [ CrossRef ] [ Google Scholar ]
MacCallum R. C., Browne M. W., Sugawara H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychol. Methods 1 130–149 10.1037/1082-989X.1.2.130 [ CrossRef ] [ Google Scholar ]
Markus H. R., Schwartz B. (2010). Does choice mean freedom and well-being? J. Consum. Res. 37 344–355 10.1086/651242 [ CrossRef ] [ Google Scholar ]
Morahan-Martin J., Schumacher P. (2000). Incidence and correlates of pathological Internet use among college students. Comput. Hum. Behav. 16 13–29 10.1016/S0747-5632(99)00049-7 [ CrossRef ] [ Google Scholar ]
Nadkarni A., Hofmann S. G. (2012). Why do people use Facebook? Personal. Individ. Dif. 52 243–249 10.1016/j.paid.2011.11.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Rudy D., Sheldon K. M., Awong T., Tan H. H. (2007). Autonomy, culture, and well-being: the benefits of inclusive autonomy. J. Res. Pers. 41 983–1007 10.1016/j.jrp.2006.11.004 [ CrossRef ] [ Google Scholar ]
Ryan R. M., Deci E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55 68–78 10.1037/0003-066X.55.1.68 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ryan R. M., Deci E. L. (2003). On assimilating identities to the self: a self-determination theory perspective on internalization and integrity within cultures. Handbook self Identity, 253–272. [ Google Scholar ]
Seidman G. (2012). Self-presentation and belonging on facebook: how personality influences social media use and motivations. Pers. Individ. Dif. 54 402–407 10.1016/j.paid.2012.10.009 [ CrossRef ] [ Google Scholar ]
Shapira N. A., Goldsmith T. D., Keck P. E., Jr., Khosla U. M., McElroy S. L. (2000). Psychiatric features of individuals with problematic internet use. J. Affect. Disord. 57 267–272 10.1016/s0165-0327(99)00107-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Shaw M., Black D. W. (2006). Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs 22 353–365 10.2165/00023210-200822050-00001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sheldon K. M., Abad N., Hinsch C. (2011). A two-process view of Facebook use and relatedness need-satisfaction: disconnection drives use, and connection rewards it. J. Pers. Soc. Psychol. 100 766–775 10.1037/a0022407 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sheldon K. M., Elliot A. J., Kim Y., Kasser T. (2001). What is satisfying about satisfying events? Testing 10 candidate psychological needs. J. Pers. Soc. Psychol. 80 325–339 10.1037//0022-3514.80.2.325 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sobel M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol. 13 290–312 10.2307/270723 [ CrossRef ] [ Google Scholar ]
Song I., LaRose R., Eastin M. S., Lin C. A. (2004). Internet gratifications and internet addiction: on the uses and abuses of new media. Cyberpsychol. Behav. 7 384–394 10.1089/cpb.2004.7.384 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Song H., Zmyslinski-Seelig A., Kim J., Drent A., Victor A., Omori K., et al. (2014). Does Facebook make you lonely? A meta-analysis. Comput. Hum. Behav. 36 446–452 10.1016/j.chb.2014.04.011 [ CrossRef ] [ Google Scholar ]
The Center for Internet Addiction. (2009). What are the types of Internet addiction ? Available at: http://www.netaddiction.com/index.php?option=com_content&view=article&id=65&Itemid=89 [accessed March 24 2010 ]. [ Google Scholar ]
Tofighi D., MacKinnon D. P. (2011). RMediation: an R package for mediation analysis confidence intervals. Behav. Res. Methods 43 692–700 10.3758/s13428-011-0076-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Vansteenkiste M., Lens W., Soenens B., Luyckx K. (2006). Autonomy and relatedness among Chinese sojourners and applicants: conflictual or independent predictors of well-being and adjustment? Motiv. Emot. 30 273–282 10.1007/s11031-006-9041-x [ CrossRef ] [ Google Scholar ]
Wallace P. (2014). Internet addiction disorder and youth. EMBO Rep. 15 12–16 10.1002/embr.201338222 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wan C., Chiou W. (2006a). Why are adolescents addicted to online gaming? An interview study in Taiwan. Cyberpsychol. Behav. 9 762–766 10.1089/cpb.2006.9.762 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wan C., Chiou W. (2006b). Psychological motives and online games addiction: a test of flow theory and humanistic needs theory. Cyberpsychol. Behav. 9 317–324 10.1089/cpb.2006.9.317 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wang H., Zhou X., Lu C., Wu J., Deng X., Hong L. (2011). Problematic internet use in high school students in Guangdong Province, China. PLoS ONE 6 : e19660 10.1371/journal.pone.0019660 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wang X. (2014). How do people participate in social network sites after crises? A self-determination perspective. Soc. Sci. Comput. Rev. 5 662–677 10.1177/0894439314525116 [ CrossRef ] [ Google Scholar ]
Westland J. C. (2010). Lower bounds on sample size in structural equation modeling. Electron. Commer. Res. Appl. 9 476–487 10.1016/j.elerap.2010.07.003 [ CrossRef ] [ Google Scholar ]
Widyanto L., Griffiths M. (2006). ‘Internet addiction’: a critical review. Int. J. Ment. Health Addict. 4 31–51 10.1007/s11469-006-9009-9 [ CrossRef ] [ Google Scholar ]
Widyanto L., McMurran M. (2004). The psychometric properties of the internet addiction test. Cyberpsychol. Behav. 7 443–450 10.1089/cpb.2004.7.443 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Xu J., Shen L., Yan C., Hu H., Yang F., Wang L., et al. (2012). Personal characteristics related to the risk of adolescent internet addiction: a survey in Shanghai, China. BMC Public Health 12 : 1106 10.1186/1471-2458-12-1106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yee N. (2006). Motivations for play in online games. Cyberpsychol. Behav. 9 772–775 10.1089/cpb.2006.9.772 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yeh Y.-C., Ko H.-C., Wu J. Y.-W., Cheng C.-P. (2008). Gender differences in relationships of actual and virtual social support to Internet addiction mediated through depressive symptoms among college students in Taiwan. Cyberpsychol. Behav. 11 485–487 10.1089/cpb.2007.0134 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yen C. F., Ko C. H., Yen J. Y., Chang Y. P., Cheng C. P. (2009). Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry Clin. Neurosci. 63 357–364 10.1111/j.1440-1819.2009.01969.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Young K. S. (1998a). Internet addiction: the emergence of a new clinical disorder. CyberPsychol. Behav. 1 237–244 10.1089/cpb.1998.1.237 [ CrossRef ] [ Google Scholar ]
Young K. S. (1998b). Caught in the Net: How to Recognize the Signs of Internet Addiction and a Winning Strategy for Recovery . New York: John Wiley & Sons. [ Google Scholar ]
Young K. S., Rogers R. C. (1998). The relationship between depression and Internet addiction. Cyberpsychol. Behav. 1 25–28 10.1089/cpb.1998.1.25 [ CrossRef ] [ Google Scholar ]
Yuan K., Qin W., Liu Y., Tian J. (2011). Internet addiction: neuroimaging findings. Commun. Integr. Biol. 4 : 637 10.4161/cib.17871 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yuen C. N., Lavin M. J. (2004). Internet dependence in the collegiate population: the role of shyness. Cyberpsychol. Behav. 7 379–383 10.1089/cpb.2004.7.379 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zhao L., Lu Y., Wang B., Huang W. (2011). What makes them happy and curious online? An empirical study on high school students’ Internet use from a self-determination theory perspective. Comput. Educ. 56 346–356 10.1016/j.compedu.2010.08.006 [ CrossRef ] [ Google Scholar ]

Compare Providers
Review Providers

Got Slow Internet? 4 Cold, Hard Facts (And 6 Solutions to Everyday Errors)

It's not always you. Sometimes, it really is them.

Jump to: Fact #1 | Fact #2 | Internet error codes | Fact #3 | Fact #4

Internet error codes

For those of us who grew up in the era of dial-up internet, waiting for pages and apps to load was a normal part of the internet experience. We figured out tricks, like playing a round or two of Solitaire or Minesweeper while pages loaded in the background. Another good trick? Schedule bandwidth-hungry updates and downloads for times everyone would be off the phone (and off the internet).

Those thumb-twiddling days are in the past for most of us, but it’s still common to run into web pages that take several seconds to load—or don’t load at all. Plenty of apps and games can also be slow, buggy, or broken entirely.

You could have a slow internet plan, outdated equipment, or unnecessary plugins dragging you down. Or, it could be out of your hands entirely.

What’s a modern day netizen to do? First, get your hands on some cold, hard facts. Then, start troubleshooting.

Is the problem your connection to the internet?

When things feel sluggish, start your troubleshooting with a quick speed test.

Fact #1: Download speeds of about 100Mbps are sufficient

It’s easy to blame your internet service provider when things seem slow. But if your speed test results show download speeds of at least 100Mbps and upload speeds of at least 3Mbps, the problem probably isn’t on your end. It’s the site or app you’re trying to reach.

Of course, speed test results on a single device are different from the speeds listed on your monthly internet plan. That’s because, first, your bandwidth is shared by all the devices in your house. Second, the speeds you pay for are “up to” speeds, meaning they are not guaranteed.

Finally, every device and every type of connection between you and the rest of the internet drops your speed by a little bit. You lose some speed over Wi-FI between your device and your router, for instance. Then there’s a little more lost between the router and the modem, etc. When you get beyond your home network, every step in the chain can slow things down.

Learn more about why your internet may be slow in our expert resource library. Then, figure out how much speed you need so you can decide whether it’s time to upgrade your plan or switch providers.

How Much Internet Speed Do I Need?

Use our internet speed calculator to get a personalized internet speed recommendation based on how you use the internet.

How many people in your household use the internet/WiFi on a daily basis?

How many devices in your home connect to the internet, including tablets, gaming consoles, and smart devices?

How many people in your household work from home?

What video quality do you use for streaming TV and movies?

How intensely does your household participate in online gaming?

Does your household download large files from the cloud or via the internet?

Our minimum speed recommendation for you:

Fact #2: The weakest link in your connections slows everything down

The first thing to remember about slow internet speeds is that every device and connection in the chain takes a little juice. We’re talking about not only the connections between your device, your modem, and the neighborhood infrastructure, but also all the connections between you and the servers of the site or app you’re trying to access.

Some sites have stellar servers and technology that minimizes speed disruptions, and they load quickly almost all the time. Other sites may have overloaded servers or code that’s not optimized for modern-day browsing. They’re always going to feel slow, no matter the speeds you’re clocking in a speed test.

Even if you’re lucky enough to have a multi-gig connection at home (and all the equipment required to support it), it’s unlikely all the servers and equipment on the other end are equally fast. Internet speed is hampered by the weakest link in the chain, and that’s often out of your control.

Website errors 101

Technical problems with the apps and sites you’re trying to reach are also common, regardless of speed availability at your home.

Sometimes, those apps and sites have serious errors, and they do more than slow you down. They stop you in your tracks!. Let’s get into some of the most common errors, plus tips on what you can do to access the sites you need.

Error: Your connection is not secure or not private

If you get a message on your phone or computer saying your connection is not private or not secure, it’s a warning that the site you’re trying to access may be unsafe. Most of the time, it just means the developers haven’t updated the site’s security certificate. Other times, it means ownership of the site can’t be verified. It could also mean there’s no encryption, meaning internet spies could potentially see anything you’re sending or receiving from the site.

On Apple computers and iPhones, this error message might read “Safari can’t establish a secure connection.”

How to fix a security or privacy error

If you think the message is a mistake, you can try refreshing the page, clearing your cache, resetting the clock on your computer, or using a different browser. If that doesn’t work, try uninstalling or disabling browser extensions and add-ons. It’s also a good idea to make sure your browser and operating system are up to date.

If nothing works, you can either bypass the error message (just don’t share any sensitive info) or reach out to the site’s owners to let them know about the problem.

What is a 404 error?

A 404 error means the page you’re trying to access doesn’t exist. If this happens to you, check your URL for spelling errors. If there are none, try typing the following before the domain name: “ https://www .” If you still get the 404 error, it means the webmaster has deleted or moved the content you’re trying to find.

Pro tip: Perform a site search

There are a lot of tricks you can try to get around unexpected 404s, but a simple site search is our favorite. Here’s how it works:

Type what you’re looking for into a search engine or the address bar in your browser
Right after your search term, type “site:” and the domain name of the site you’re searching. For example, type the following to look for instances of T-mobile on our site: “t-mobile site:highspeedinternet.com”
Press enter for a list of all pages within the site that list your search term

What is a request timeout error?

A request timeout or idle connection error (usually with the error code 408) means things are taking too long to load. This one is a little shifty, because it can happen either when the site you’re trying to load is busy or when you have a slow connection on your network.

How to resolve a 408 timeout error

First things first, refresh the page (press Command+R or CTRL+R on your keyboard). If that doesn’t fix the error, make sure you’ve typed in the URL correctly (including slashes and hyphens). Next, deactivate or uninstall plugins on your browser and refresh again. That failing, clear your cache and start from square one.

If none of that works and only one site is throwing up a 408 error, the problem is probably with that site. You can come back later, try on a different device or Wi-Fi connection, or contact the site’s owner.

What does “unavailable for legal reasons” mean?

In some parts of the world, you may get a 451 error that says this page you’re trying to reach is censored or illegal. That’s not very common in the U.S., but you might see this error if the page isn’t available due to a lawsuit or legal action.

On smart TVs, cable boxes, and streaming devices, you could get this error if you’re trying to watch a game or show that isn’t licensed in your area.

What to do about a 451 error?

Sometimes, you’ll get this error even if the site or show you’re trying to access is legal where you live. It happens because of the way internet traffic is routed around the world. To get around the error, you can use a VPN to mask your IP address.

Any VPN would slow your internet speed, but some VPNs are better than others. Check out our list of VPNs worth your money .

What is a Bad Gateway error?

When your page won’t load due to a 502 error, it means there was a problem when one server tried to send a request to another server. We won’t bore you with a full technical explanation, but for most users, it means the site is broken. However, it may also mean the server is overloaded.

What to do about a 502 error?

If you run into a 502 error on your internet escapades, the first step is to reload the page. If that doesn’t work, try uninstalling plugins, clearing your cache , and then going back to the page. If nothing works, come back later or simply abandon your quest on that particular page.

What is an HTTP 503 error?

When you get a 503 error while browsing online, it means the service is currently unavailable. Usually, it’s a temporary problem and you’ll see a user-friendly page with info about when the service will be back up.

What to do about a service unavailable error as a user?

You can try fixes like refreshing the page, turning off browser extensions, and clearing your cache. If the problem is truly on the server end, though, you may just have to wait until the site’s developers can resolve it.

Pro tip: Play solitaire while you wait

Even if you aren’t on dial-up or DSL, you may need a way to pass the time while you wait for slow or broken pages. For a blast from the past, play Solitaire free or check out this free Minesweeper game .

Fact #3: Sometimes the problem really is your home internet

As you can see, slow internet isn’t always in your control. But if you discover your internet connection is the problem, there are steps you can take.

1. Research your plan’s speed

Sometimes, you may just need a more expensive internet plan with a faster speed. Other times, you may have adequate speeds but run into data caps and throttling because you don’t have enough monthly data. This problem is especially common if you’re using a satellite connection or mobile internet, but many cable internet companies also have data caps that result in monthly overage charges.

Learn which internet providers impose data caps .

2. Find out about infrastructure updates near you

Sometimes, temporary outages and severe slowdowns could be related to maintenance upgrades by your local provider. These upgrades are annoying, but they mean faster internet is coming! We highly recommend adding your provider’s app to your smartphone and signing up for email and text message alerts to stay on top of things.

Find out how internet infrastructure upgrades may affect you.

3. Compare providers near you (and see if it’s time to switch)

It used to be common to have very few internet choices in any given location due to provider monopolies and technology limitations. That’s still true in some areas, but new options are cropping up all the time. Even if you have only one fully wired internet provider in your neighborhood, you could have access to 5G home internet from T-Mobile or even satellite service from Starlink .

Ready to switch internet providers?

Enter your zip code to see what’s available near you.

Fact #4: There’s more you can do to speed up your internet

If you can’t upgrade your speed, adjust your data cap, or switch providers, there are few other things you can do that might speed up your system. You can read all about it in this internet speed troubleshooting guide from our expert resource library, or check out our 10 simple steps for faster Wi-Fi .

In the meantime, here’s a TL;DR of our expert tips:

Guard against malware and viruses: Use your built-in firewalls and antivirus apps, and set up automatic updates for everything from apps to operating systems, routers, and modems.
Optimize your router or gateway: Choose a good spot for your gear, and make sure you aren’t using an overcrowded Wi-Fi channel.
Reboot your gear: Whether it’s turning your devices off and on again or fully disconnecting them from electricity for several seconds and plugging them back in, a reboot can do wonders for internet speeds.
Remove unnecessary devices: Don’t be too generous with your Wi-Fi password, and remove any devices or apps that may be siphoning bandwidth unnecessarily.

Speedy internet is a thing of beauty, and we’re serious about helping folks across the U.S. access the best possible options for their homes and businesses. Browse the rest of our resource library for more help, and good luck out there!

Author - Chili Palmer

With more than 15 years' experience in the coverage of tech and consumer issues, Chili Palmer holds a bachelor's degree in print journalism from Weber State University. Before joining highspeedinternet.com, she wrote about home internet and cable TV for Switchful.com. Her writing and editing work has also appeared in the Idaho Business Review, Utah Business magazine, Top Ten Reviews, Deseret News, and more. Previously writing under the name Rebecca Palmer, Chili is passionate about providing accurate and accessible information any time you're trying to connect … whether you already speak geek or just got your first smartphone.

woman using computer at desk with pictures hanging above her on the wall

Apple for Business
Blackmagic Design
Epson Printers
Canon Printers
LG Displays

If Your Internet Connection Is Slow, Research These Three Numbers

As pandemic-related restrictions ebb and flow, many people continue to work from home at least some of the time. A key requirement for successful remote work is a fast, solid Internet connection. Unfortunately, it’s all too common for Internet connections to degrade over time as cables endure a variety of insults ranging from water seeping into connectors to squirrels gnawing through insulation. You might be surprised at how variable such problems can be—we once saw an Internet connection that dropped out infrequently; it turned out that squirrels had damaged just one pair of wires in the cable, and the Internet provider’s system hopped between pairs depending on other neighborhood traffic.

How do you know if something in your Internet connection needs fixing? If the entire connection goes offline periodically, even if it comes back on its own, that’s a hint. Videoconferencing apps and streaming video services can also provide useful warnings—if the picture often pixelates or pauses, or if the audio falls out of sync with the video, that’s another indication that something may be wrong. Finally, if you use a VPN, Internet connectivity issues can cause all sorts of weird problems.

When you notice such problems, you can perform a simple test that may shed some light on the situation. First, make sure to stop any audio or video streaming or large downloads. Then go to speedtest.net and click the Go button. Speedtest will run some tests to come up with three numbers:

Download: Your download performance, generally measured in megabits per second (Mbps), is the most important and determines how quickly your computer can receive data from the Internet for downloads and streaming video.
Upload: Conversely, your upload performance, also measured in megabits per second, determines how fast you can send data to the Internet. It’s almost always much lower than download performance, but sufficient upload bandwidth is essential for your audio and video to be clear and smooth in a videoconference.
Latency: This number, which Speedtest calls “ping,” measures the amount of time in milliseconds to send a single packet to the destination server and receive it back again. The lower the latency, the more responsive communications will be, which is all-important for online gaming. Very long latencies can also cause problems for VPNs.

(You can also use other similar services, such as Netflix’s fast.com or Google’s built-in tester . Apple even built a networkQuality tool into macOS 12 Monterey—run it at the command line in Terminal—though it has an unusual Apple Network Responsiveness test that replaces the latency test.)

What should these numbers be? When you signed up for a plan with your Internet service provider (ISP), you should have been quoted download and upload numbers, such as 200 Mbps down and 10 Mbps up. If you don’t know what those numbers should be, ask your ISP. It’s not uncommon for download and upload speeds to vary somewhat from what you were promised, but they should generally be close.

Latency is trickier, and ISPs won’t promise anything specific. The lower the latency number, the better. Speedtest suggests that times under 59 milliseconds are good for gaming, times between 60 and 129 milliseconds are acceptable, a range of 130–199 milliseconds is problematic, and 200-plus milliseconds won’t be usable. There’s no way to know what latency numbers might cause other problems, but we’d pay attention to anything over 100 milliseconds.

Let’s assume your numbers aren’t what they should be—or at least what you’d like them to be. Don’t complain to your ISP right away. Instead, try these steps:

Run the test a few more times, preferably at different times of the day. (If you create an account with Speedtest, it will track your tests so you can see how they change.) You may discover that performance is slow only during peak usage hours.
Run more tests using different Speedtest servers or even different testing services. You’re testing the performance between you and a particular server, and it’s possible that server is having problems that others won’t share.
If you have other devices, run tests from those as well. Don’t assume you’ll get identical results because different devices may have varying capabilities. If you notice differences between devices that use Wi-Fi and those connected via Ethernet, that might point to your Wi-Fi router as a bottleneck. That’s not common with modern Wi-Fi routers, but if you’re still relying on an old AirPort Express, it could be slowing you down.
Restart your devices, and if that doesn’t make a difference, restart or power cycle your router and modem as well.

If your performance problems persist through all those troubleshooting tests, the problem is likely in the physical connection, and only your ISP can address that. When you call, be sure to share the testing numbers and any commonalities—times of day, for instance—that you observed. With luck, they’ll be able to confirm your issues and dispatch a technician to evaluate the physical connection for problems.

(Featured image by iStock.com/gorodenkoff)

Social Media: Slow or unreliable Internet connection? It could be your ISP—read on to find out how to test your connection and evaluate the results.

performance
troubleshooting

Our Company

Experience & Certifications
Hours & Locations
Philanthropy
Job Opportunities

MBS Resources

MBS Downloads
Service FAQ
Return Policy
Terms and Conditions
Privacy Statement
Newsletters
Subscribe to our Newsletter

Main Office

611 S. Frederick Ave. (Rt. 355) Gaithersburg, MD 20877

Phone: (301) 590-2555 Toll-Free: (888) 354-0100 Online Sales: (301) 518-9545 Fax: (301) 590-8142

Our expert, award-winning staff selects the products we cover and rigorously researches and tests our top picks. If you buy through our links, we may get a commission. How we test ISPs

Home Internet

Why Your Internet Is Down and How to Quickly Get Back Online

Internet down again? It's not always your provider's fault. Here's how to identify and rectify the five most common internet connectivity issues.

Losing your internet connection can disrupt your whole day. Here's how to figure out what's going on and fix it.

There's never a good time for your internet to go out, so when it happens, you want to get your home back online as soon as soon as possible. Unless there's a major outage from your internet service provider, identifying the problem and restoring your connection is often quick and easy.

You'll find the most common reasons why your internet might go out below, followed by ways to resolve each issue, if possible. For more Wi-Fi tips, check out why your router may be in the wrong place and how to find free Wi-Fi anywhere in the world .

Common causes of home internet outages

Here are some of the top causes your internet may have dropped -- we'll dive into solutions for each below.

Locating local internet providers

1. Modem/router malfunctions

2. Inadequate speeds or equipment

3. Hacking or network issues

4. Bad weather

5. ISP service outages and network congestion

Narrowing down the exact issue can take a bit of investigating and troubleshooting. Start by verifying that the connection issue isn't specific to a single website, server or device.

If you've lost your Netflix connection halfway through a show, check if other streaming services are still accessible and working. If so, the problem likely lies with Netflix and not your internet connection. If you're having an issue connecting to other streaming services, it could be that the smart TV or streaming device is to blame. Try streaming on another device, if possible, to verify that an internet outage is the culprit.

Modem and router issues

When your home internet connection goes out, it's often due to a hiccup with your modem and/or router. The solution is simple: Restart your equipment by unplugging it, waiting 30 seconds or so, plugging it back in and allowing it to reboot. More often than not, this will resolve your Wi-Fi outage.

When restarting your router, I'd recommend cutting power by unplugging it instead of pressing or holding any buttons on the device itself. Doing so can prompt the device to do a hard reset, returning it to factory settings and erasing your Wi-Fi network settings. Granted, the reset will likely re-establish your internet connection, but you'll also have the extra task of setting up your Wi-Fi again.

Also, keep in mind that your device may have a battery backup. If the lights on your modem or router don't go out when you unplug it from the power source, check to see if there are batteries installed somewhere and temporarily remove them when restarting your device.

Slow speeds or unreliable equipment

Maybe your internet isn't necessarily "out" -- it just can't keep up with what you're trying to do or where you're doing it.

Constant buffering, excessive lagging, Wi-Fi "dead zones" and other connectivity issues could result from insufficient speeds, bandwidth or Wi-Fi coverage to handle all your devices. There are two ways to remedy the situation: Scale back your internet use or make some upgrades.

Consider the internet speeds you need and determine if your current plan can deliver those speeds. If your plan lacks the speeds you need, upgrading to a faster plan (assuming one is available) will be your best option. Many cable and fiber internet providers offer speeds up to 1 gigabit per second or higher , which is plenty of speed for the average home.

On the other hand, if you feel your current plan should meet your needs, it's possible your equipment is to blame. Conduct a few speed tests around your home to gauge what speeds you are getting and where the Wi-Fi signal might not be as strong. Sometimes simply relocating your router to a more efficient spot will improve connection quality and eliminate or at least mitigate any dead zones.

Otherwise, you may want to invest in a better router or Wi-Fi extenders to boost the Wi-Fi signal throughout your home. If you rent equipment from a provider, call to ask about getting a better device .

Try adjusting your router settings

Your router should allow you to steer connected devices to a specific pod or extender, if you have them, and between 2.4GHz and 5GHz bands. You'll get a stronger signal on the 5GHz band, but only if your device is within range (the 5GHz range is shorter than 2.4GHz) and if there aren't too many other devices connected to 5GHz. So, if your connection quality is weak on a particular device, try switching bands on the device or moving some of the other devices off the band you're using.

Use a wired connection

Connecting directly to your modem, router or pods/extenders using an Ethernet cable will be your best bet for establishing and maintaining a strong connection. If possible, use a wired connection for the most bandwidth-hogging devices, like smart TVs and gaming consoles. Not only will this often provide a better, faster connection, but it will also take some of the strain off your Wi-Fi network.

Hacking or malware

A less likely but still possible cause of an internet outage is a compromised network . If hackers gain access to your Wi-Fi network, they could completely restrict your internet access to any or all devices.

If you suspect someone has gained unauthorized access to your network, immediately go to your router settings and recreate your Wi-Fi network with (preferably) a different network name and (definitely) a different password -- one with some complexity or randomness that will make it difficult for a hacker to figure out.

Along with creating a strong password, be sure to keep all firmware on your router and any connected devices up to date to help prevent hacking attempts. Installing antivirus software will also help keep your devices protected. Many ISPs offer virus and malware protection at no extra cost.

Bad weather

Yes, Mother Nature can mess with your internet connection . Some internet connection types are more prone to internet outages than others during bouts of bad weather, but hard rain, a violent thunderstorm or even heavy cloud coverage could interfere with your signal.

Satellite internet is the most vulnerable to internet outages caused by weather, but a power outage can knock any connection type offline. Having a modem and router with a battery backup may help keep you connected during power outages, though they will be useless if the power outage is preventing internet service from reaching your modem in the first place.

If you have satellite internet, a rain guard, snow shield or dish heater can help prevent outages due to bad weather in the immediate area of your home. Signal interference can happen anywhere along the path between the satellite and your dish, however, and heavy cloud coverage or rain could have an impact on your connection even if it's miles away. There's not much you can do about an internet outage in that case, unfortunately; you'll just have to wait for the signal to return.

ISP outages and network congestion

Despite the negative impression many people have about their internet providers , widespread ISP outages are uncommon and outages at a single residence are virtually unheard of (unless, of course, you forgot to pay the bill). Still, it's possible that the provider is having issues.

If your internet is completely out and you've already tried restarting the router, check your provider's social media pages, official website or sources like downdector.com for updates and outage reports. You can also call customer service, but be prepared for a long wait on hold.

Other than confirming your ISP is having problems, there's nothing you can do in such situations other than wait for service to return. Outages are bad publicity , so rest assured your ISP is doing everything in its power to restore service as quickly as possible.

Outages are rare but network congestion could be a much more frequent problem and, while it won't always knock your connection out completely, it can certainly cause slowed speeds. Cable, DSL and satellite internet are vulnerable to network congestion, as is 5G home internet. T-Mobile acknowledges network congestion can lead to slowed speeds, stating that "during congestion, Home Internet customers may notice speeds lower than customers using other T-Mobile services due to data prioritization."

Network congestion means the speeds coming to your home are slowed, so there isn't much you can do about that other than wait for the congestion to clear. You can, however, make the most of the speeds you are getting by placing your router in an optimal location, adjusting your Wi-Fi settings or using an Ethernet connection, as mentioned above.

Internet still out? Here's what else you can do

Aside from the tips listed above, there are a couple of ways you may be able to get back online.

The first is by using your mobile connection. Your phone will likely automatically switch to cellular service if your Wi-Fi goes out, so you'll be able to use your phone just like you would if you were away from home. Keep in mind, however, that doing so will use up your mobile data.

Additionally, some phones, carriers and plans allow you to create a Wi-Fi hotspot. It won't power your home the same as your router, but it will enable you to connect a few devices until your home network comes back.

Second -- and perhaps only applicable for longer outages or urgent internet needs such as submitting a school assignment on time -- would be to find a public Wi-Fi hotspot. Your local public library, coffee shop or restaurant, among many other public places, may offer free Wi-Fi.

Be mindful that using a public Wi-Fi connection is not as secure as your home network, so consider using a VPN or avoid any activity that involves sensitive data (passwords, banking info, doing your taxes, etc.) while on a public network.

Internet outage FAQs

Why does my internet keep going out.

There could be a number of factors that affect your internet connection. First, and most likely, is problems with your equipment. Restarting your modem/router will often resolve the issue.

Other reasons why your internet may keep going out include inadequate speeds, network congestion and inclement weather. It's possible that your provider is experiencing a service outage, but for frequent connection disruptions, I would look to the previously mentioned causes, starting with your router.

Can I get a partial refund for internet provider outages?

Many providers do offer compensation for prolonged or frequent outages. Spectrum, for example, will provide "proportionate credit for those qualifying outages that last for 4 or more consecutive hours." Call to report the outage as soon as possible and monitor how long it lasts before requesting a refund.

Will a power outage knock out my internet service?

Not always, but probably. When the power goes out, it won't necessarily keep internet service from reaching your home, but it can certainly limit your ability to use the internet. Unless your modem and router have a battery backup, a power outage will disable those devices, rendering you unable to connect to the internet.

Home Internet Guides

Best Internet Providers in Los Angeles
Best Internet Providers in New York City
Best Internet Providers in Chicago
Best Internet Providers in San Francisco
Best Internet Providers in Seattle
Best Internet Providers in Houston
Best Internet Providers in San Diego
Best Internet Providers in Denver
Best Internet Providers in Charlotte NC
Google Fiber Internet Review
Xfinity vs Verizon Fios
Verizon 5G vs. T-Mobile Home Internet
Verizon Internet Review
Xfinity Internet Review
Best Rural Internet
Best Cheap Internet and TV Bundles
Best Speed Tests
AT&T Home Internet Review
Best Satellite Internet
Verizon 5G Home Internet Review
T-Mobile Home Internet Review
Best Internet Providers
Frontier Internet Review
Best Mesh Wi-Fi Routers
Eero 6 Plus Review
TP-Link Review
Nest Wi-Fi vs. Google Wi-Fi
Best Wi-Fi Extender
Best Wi-Fi 6 Routers
Best Wi-Fi Routers
What is 5G Home Internet?
Home Internet Cheat Sheet
Your ISP May Be Throttling Your Internet Speed
How to Switch ISPs
Internet Connection Types
Internet for Apartments
Top 10 Tips for Wi-Fi Security
How to Save Money on Your Monthly Internet Bill
How Much Internet Speed Do You Need?
AT&T Internet Promo Codes
Verizon Fios Discounts
Comcast XFINITY Codes

Solar Eclipse 2024

Will the Solar Eclipse Affect Cell Service?

F rom New York to Indiana , state officials across the country are warning of potential cell phone outages during the day of the total solar eclipse on April 8, as millions of people flock to the path of totality to witness—and post— the celestial event.

Cell service can be disrupted when a large number of people overload the system with calls, messages, and other data-sucking activities— a situation that might arise in areas along the path of totality .

“A typical overload situation, such as some experience at a major football game, major sporting event or pop concert, is likely to occur in many places where the network is not provisioned for such an unusually large crowd,” Theodore Rappaport, director of NYU Wireless, a multidisciplinary research center focused on the future of wireless communications and applications, says in an email.

Read More : How to Use Your Smartphone to Take Photos of the Solar Eclipse

Though wireless companies have spent the past few years upgrading their wireless networks, rural areas are still particularly vulnerable to disruptions, Rappaport says, as the grid might not be equipped to handle the sudden influx of tourists. “Imagine a rural cellular system which is usually designed for 1,000 customers. Now, if 10,000 customers show up in the particular rural location, it is likely the much greater customer base will have difficulty accessing bandwidth, and be blocked temporarily until the crowd dissipates,” he says.

That means “anything more than texting” might be difficult until the congestion eases, he adds.

Read More : The Eclipse Could Bring $1.5 Billion Into States on the Path of Totality

During the 2017 eclipse, Verizon, AT&T, and T-Mobile all dispatched portable towers to boost coverage along the eclipse’s path. This year, Verizon, T-Mobile, and AT&T said they did not anticipate that the eclipse will significantly impact their networks.

“We do not expect any impact from the 2024 solar eclipse on the operation of our network. In areas where people may gather to experience this event, we’re confident the additional capacity we’ve layered into the network over the past few years will accommodate any increases in data usage,” Verizon said in a statement to TIME.

“T-Mobile has increased investments in network hardening by more than 30% over the past two years to reduce service interruptions during weather, disaster and major tourism events (like the solar eclipse), adding fixed backup generators at critical sites nationwide,” the network said in a statement .

Read More : Why These Passengers Are Flying up to 30 Hours to See Four Minutes of the Eclipse

"The eclipse itself will have no direct operational impact on our wireless network," AT&T said in a statement. "Our network has drastically changed since the last solar eclipse in 2017. Our customers now have the benefit of AT&T 5G which provides unique experiences, faster speeds and capacity to do more of the things our customers like. We expanded our 5G network to reach more than 295 million people in nearly 24,500 cities and towns in the U.S. Our higher-speed mid-band 5G+ spectrum alone covers more than 210 million people. Our bandwidth has increased significantly in the last few years, providing for an improved experience on our network as a result."

Rappaport says disruptions are likely to be “sporadic and temporary,” but if you’re looking to err on the side of caution, consider downloading any important information you might need— like your hotel address or directions, and stay near a Wi-Fi access point if you are depending on coverage.

One of the biggest impacts cell service disruptions could have on eclipse viewers is making them have to wait a little longer to post images of the eclipse on social media. “I expect in high population centers or places where large crowds gather in peak viewing areas, there will be outages,” Rappaport says, “particularly during the five minutes or so where people are immersed in darkness and trying to share the experience over their phone.”

Correction, April 10

The original version of this story mischaracterized AT&T's response to a request for comment. The network did send a statement and the text has been updated to reflect that.

More Must-Reads From TIME

Exclusive: Google Workers Revolt Over $1.2 Billion Contract With Israel
Jane Fonda Champions Climate Action for Every Generation
Stop Looking for Your Forever Home
The Sympathizer Counters 50 Years of Hollywood Vietnam War Narratives
The Bliss of Seeing the Eclipse From Cleveland
Hormonal Birth Control Doesn’t Deserve Its Bad Reputation
The Best TV Shows to Watch on Peacock
Want Weekly Recs on What to Watch, Read, and More? Sign Up for Worth Your Time

Write to Simmone Shah at [email protected]

Expert Briefing: Research Update: Working to Halt PD

April 10, 2024

There is still a lot we don’t know about Parkinson’s disease (PD), and through research, efforts are being made to close the gaps in our knowledge and understanding. In addition to the development of new therapies for symptom management, research also seeks to find ways to stop progression and ultimately end PD. During this Expert Briefing, we will learn about current research that is taking various approaches to develop a treatment that may significantly slow PD’s progression.

Download Slides

Lorraine Kalia, MD, PhD, FRCPC, Associate Professor Division of Neurology, Department of Medicine at the University of Toronto Toronto Western Research Institute Tanz Centre for Research in Neurodegenerative Disease

Parkinson's Connection

Personal information.

2025 Subaru Forester costs $2,900 more, starts at $31,090

Email This Page

The redesigned 2025 Subaru Forester arrives on dealer lots later this spring with a new look, updated tech, and a starting price that increases $2,900 over the 2024 model, Subaru disclosed Thursday.

That brings the popular compact crossover's starting price to $31,090, including a $1,395 destination fee, for the base 2025 Forester. That's still about $300 less than a base Toyota RAV4 LE with AWD.

Every Forester runs on a 2.5-liter flat-4 making 180 hp and 178 lb-ft of torque. A continuously variable automatic transmission doles out power to all four wheels for standard all-wheel drive that Subaru says is more responsive, likely due to software upgrades. Subaru marries the carryover powertrain with a new dual-pinion steering rack borrowed from the Subaru WRX and a stiffer chassis with a quieter cabin that promises to make for improved ride quality.

Subaru equips each Forester with an enhanced version of its EyeSight radar and camera safety suite that now has a wider field of view and updated software that should make it operate more smoothly and responsively. The driver-assist features include adaptive cruise control, automatic emergency braking with cyclist and pedestrian detection, and active lane control. Other safety features include adaptive LED headlights with automatic high beams and a rear-seat reminder. Blind-spot monitors are available on the Premium grade, and standard on Sport, Wilderness, Limited, and Touring grades.

The base Forester has 17-inch alloy wheels, keyless entry, cloth upholstery, and dual 7.0-inch touchscreens for infotainment and navigation. Apple CarPlay and Android Auto compatibility come standard, as do USB-A ports and a satellite radio trial. It appears Subaru has finally ditched the CD player.

2025 Subaru Forester

The $33,390 Premium upgrades to a tablet-style 11.6-inch touchscreen with wireless smartphone compatibility, USB-A and USB-C ports, a wifi hotspot, heated front seats and side mirrors, a 10-way power driver's seat, roof rails, a sunroof, and an off-road mode with hill descent control. Add a power tailgate and blind-spot monitors for $1,200 more.

The 2025 Forester Sport costs $35,890 and flashes 19-inch wheels trimmed in bronze, some chrome and bronze badging inside and out, and a $1,700 option that adds Harman Kardon audio, and a couple of drive modes. For that sporting vibe, the Sport also has paddle shifters that mimic an 8-speed automatic.

The Forester Wilderness carries over unchanged, and carries the same $36,315 price. It stands taller with 9.2 inches of ground clearance (8.7 inches is standard) on larger coil springs and shocks, and wears even more body cladding to protect the body from off-road elements. It rides on black 17-inch wheels wrapped in Yokohama Geolander tires, and a full-size spare rides under the cargo area.

The Limited swaddles the Forester in more comforts, such as perforated leather upholstery, a power passenger seat, a heated and leather-wrapped steering wheel, and a surround-view camera system. It rolls on 18s and costs $37,390.

The $41,390 Touring tops the lineup with memory front seats that are heated and cooled, heated rear seats, a lower profile roof rack, and the Harman Kardon sound system.

Imported from Japan, the 2025 Subaru Forester goes on sale later this spring.

Recommended Galleries

2024 Chevy Silverado EV RST: $96,495, 440-mile range

People who read this also read

Nissan Rogue vs. Honda CR-V: Compare Crossover SUVs

Ford plans hybrids across lineup, delays 3-row EVs

2025 Subaru Forester debuts at Los Angeles Auto Show

2024 chevy silverado ev rst: $96,495, 440-mile range, related articles.

Compare cars
Cars For Sale
Trucks For Sale
Resource Center
How We Rate Cars
Buy Auto Leads
Privacy Policy
Terms of Use
Manage Preferences

IMAGES

Slow Internet Connection? 10 Ways to Speed up your Internet!
Why is internet slow? Ways to fix sluggish connections
Culprits of Slow Internet Connection
7 Reasons Why Your Internet is Slow (and how to fix it)
Slow Internet Connection, Here is What to Check
Results of the number of requests on the server (slow Internet

VIDEO

how to solve slow internet connection #wifi6 #wifi6router
Slow internet connection #meme #funny #shortsfeed #shorts
slow internet connection at update pa Ragnarok Origin
Slow Internet Connection-Combat Master #lag #alfabravoinc #combatmaster #steam #shorts #music
slow internet connection who to booyah
Slow internet connection be like on MH rise sunbreak

COMMENTS

Are slow internet connections holding back American schools?
Given this research, slow internet speeds are likely not harming student outcomes. However, schools need access for basic administrative purposes, to conduct online tests, offer support ...
Poor Internet connection leaves rural students behind
Slow Internet connections or limited access from homes in rural areas can contribute to students falling behind academically, according to a new report from Michigan State University's Quello Center. The educational setbacks can have significant impacts on academic success, college admissions and career opportunities.
Effects of Lack of Internet Access in the Academic Performance of STEM
The major goal of this research was to see how internet usage affects students' academic performance particularly when it comes to increasing their grades. ... slow internet server and problem of paying for online services. The study recommends that the university should embark on Information literacy/sensitization programme for student's ...
PDF Internet Connection and Learning Device Availability of College ...
regards to internet connectivity and the use of gadgets or devices for online learning. In a study by Fabito et al., (2020) the group revealed that one of the three barriers and challenges that students encountered in online learning was a good internet connection. Another related concept from
Student Experiences with Connectivity and Technology in the Pandemic
1Student Experiences with Connectivity and Technology in the Pandemic. 2Not All Internet Access Is Created Equal. 3The Struggle Is Real. 4Get Creative or Go Without. 5Some Devices Aren't Up to the Task. 6Students Usually Choose "Do-It-Yourself" Device Support. 7Methodology and Acknowledgments.
WiFi at University: A Better Balance between Education Activity and
The WiFi network is built on CISCO hardware and software which allows statistics on network traffic to be gathered using NetFlow, see Fig. 2.The Netflow traces of internet traffic can be used to identify levels of activity and patterns of behavior on the internet [47].The data collected over time represent a comprehensive log that allows an accurate, longitudinal assessment of internet usage ...
Disparities in Technology and Broadband Internet Access across Rurality
Access to broadband Internet service ("broadband") has recently been identified as a social determinant of health, alongside access to health care, economic stability, education, and community and social context, among others. 1,2 The coronavirus disease (COVID-19) pandemic has underscored the necessity of reliable broadband access for both health care and education needs. 3 Indeed ...
Everything You Need to Know About Slow Internet Speeds
Stand near your router and use the app to run a speed test. Move to a room farther away from the router and run the speed test again. Compare the results. Less than 15 megabits a second is pretty ...
Lack of internet connections deepen inequity in education and health
By Liam Bohen-Meissner Medill Reports Access to virtual education and telehealth underscore the digital divide in Chicago and elsewhere exacerbated by the onset of the COVID-19 pandemic. When coupled with limited in-person activity, a lack of broadband internet potentially forces millions of students to fall behind in school and millions of people to forgo medical […]
Internet use and Problematic Internet Use: a systematic review of
However, the line between Internet Use (IU) and Problematic Internet Use (PIU) is noticeably being overstepped; with high use of the Internet to the extent of 'addiction' being the focus of much global research, and 'Internet Gaming Disorder' being proposed as a condition requiring further research by the American Psychiatric ...
Blended Learning in Efl Classrooms With Slow Internet: Insights From
INTERNET: INSIGHTS FROM TEACHER S AND STUDENTS. Af‟idatul Husniyah, S.Pd., M.Sc. State Polytechnic of Malang, Jl. Soekarno Hatta No.9, Malang, Indonesia. Corresponding email: afidahusniyah@gmail ...
A Study on The Internet Connectivity in The Philippines
In 2015, Thailand had an average Internet speed of 7.4 Mbps, Sri Lanka 7.4, and Malaysia 4.3. Meanwhile, the Philippines had a meager average Internet speed of 2.8 Mbps, placing the country at 104 ...
University students' usage of the internet resources for research and
Yet, slow internet connection and inadequate institutional internet facilities were highlighted as the constraint to proper access and usage of the internet. Additionally, Oduwole (2004) ... Adogbeji O.B., Akporhonor B.A. The impact of ICT (internet) on research and studies: the experience of Delta State University students in Abraka, Nigeria ...
8 Reasons Why Your Internet is Slow (and How to Fix It)
Step 2: Wait 60 seconds. Step 3: Plug the power cable back into the back of the equipment. Step 4: Wait for the equipment to reboot (this can take up to 20 minutes). 2. Your home network is congested. Your slow speeds might be the result of trying to do too much at once on your internet connection.
The disconnected: COVID-19 and disparities in access to quality
The COVID-19 pandemic forced many higher education institutions (HEIs) across the world to cancel face-to-face teaching, close campus facilities, and displace staff and students to work and learn from home. Given the persistent nature of the pandemic, many HEIs have continued to deliver courses online and/or use a blended learning approach. However, there are concerns around differences in ...
Effect of Internet on Student's Academic Performance and Social Life
Abstract. The use of the internet has a huge impact on student achievement. This study was conducted to determine the effect of internet use on academic achievement, social life, and student activities in Bandung. This research will be very helpful for students, researchers, and curriculum developers to know the relationship of internet usage ...
Why Is the Internet so Slow?!
Abstract. In principle, a network can transfer data at nearly the speed of light. Today's Internet, however, is much slower: our measurements show that latencies are typically more than one, and ...
A basic need theory approach to problematic Internet use and the
The ethical standards of this study were approved by the Research Subcommittee of the Department of Counselling and Psychology, Hong Kong Shue Yan University. ... A review of the research on Internet addiction. Educ. Psychol. Rev. 17 363-388 10. ... and connection rewards it. J. Pers. Soc. Psychol. 100 766-775 10.1037/a0022407 [Google ...
PDF Research Paper EFFECTS OF INTERNET ON THE ACADEMIC PERFORMANCE OF
usage of internet which include slow internet speed and lack of stable power supply. It was recommended among ... (2016) conducted a research on Internet Usage on the Academic Performance of Undergraduates Students at University of Abuja, Nigeria. Results gotten from the research shows that 111(55%) of respondents use the internet every day ...
Got Slow Internet? 4 Cold, Hard Facts (And 6 Solutions to Everyday
Fact #1: Download speeds of about 100Mbps are sufficient. It's easy to blame your internet service provider when things seem slow. But if your speed test results show download speeds of at least 100Mbps and upload speeds of at least 3Mbps, the problem probably isn't on your end. It's the site or app you're trying to reach.
Impacts of Slow Internet Connectivity on the Academic ...
Impacts of Slow Internet Connectivity on the Academic Performance of Grade 11 STEM Students. Introduction. The use of technology changes the way things are done, including secondary organizations where the teaching and learning involved a tremendous amount of improvement—knowing that the internet is an essential tool for facilitating academic activities in secondary institutions in different ...
The impact of internet access on research output
The purpose of this paper is to investigate the relationship between internet penetration and research output. Using a country-level panel dataset, we found that internet penetration boosts research output of an economy. As a country has better access to the Internet, its overall research productivity also increases.
If Your Internet Connection Is Slow, Research These Three Numbers
If Your Internet Connection Is Slow, Research These Three Numbers Written by Adam Engst Tuesday, 01 February 2022 10:04 - (838) As pandemic-related restrictions ebb and flow, many people continue to work from home at least some of the time. A key requirement for successful remote work is a fast, solid Internet connection.
Why Your Internet Is Down and How to Quickly Get Back Online
Modem and router issues. When your home internet connection goes out, it's often due to a hiccup with your modem and/or router. The solution is simple: Restart your equipment by unplugging it ...
PDF A Study on The Internet Connectivity in The Philippines
Using articles and research of international organizations and content from official websites of the Philippine government, this paper has carefully examined the slow Internet connectivity and the high ... The purpose of this study is to evaluate the cause of the slow Internet connection in the Philippines and the high cost that end-users pay ...
HP PCs
In Windows, search for and open the Microsoft Store . In the Microsoft Store, search for network speed test, and then select the Network Speed Test app in the search results. Click Get, and then wait while Windows downloads and installs the app. Click Open to open the app. If prompted to let the app access your precise location, click Yes .
Will Solar Eclipse 2024 Affect Cell Service?
By Simmone Shah. April 7, 2024 7:00 AM EDT. F rom New York to Indiana, state officials across the country are warning of potential cell phone outages during the day of the total solar eclipse on ...
Expert Briefing: Research Update: Working to Halt PD
In addition to the development of new therapies for symptom management, research also seeks to find ways to stop progression and ultimately end PD. During this Expert Briefing, we will learn about current research that is taking various approaches to develop a treatment that may significantly slow PD's progression. Download Slides.
2025 Subaru Forester costs $2,900 more, starts at $31,090
April 4, 2024. The redesigned 2025 Subaru Forester arrives on dealer lots later this spring with a new look, updated tech, and a starting price that increases $2,900 over the 2024 model, Subaru ...

Are slow internet connections holding back American schools?

How fast is fast enough?

Table 1: Recommended speeds for online activities

Does E-Rate benefit students?

The distribution of internet speeds

Figure 1: Average KBPS per student by county

The future of E-Rate

Effects of Lack of Internet Access in the Academic Performance of STEM Students

Related Papers

RELATED PAPERS

Everything You Need to Know About Slow Internet Speeds

First, diagnose the problem.

If it’s your router, here’s what to do.

If it’s your service provider, there’s not much to do.

Tech Fix: Solving Your Tech Problems

Lack of internet connections deepen inequity in education and health care during COVID-19

The disconnected: COVID-19 and disparities in access to quality broadband for higher education students

Introduction

Research questions

Institutional and policy context

Broadband connectivity in Ireland

Materials and methods

Discussion and implications

Limitations and future research directions

Availability of data and materials

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Supplementary Information

Rights and permissions

About this article

Share this article

Effect of Internet on Student's Academic Performance and Social Life

Article metrics

Share this article

Author affiliations

A basic need theory approach to problematic Internet use and the mediating effect of psychological distress

Kenneth S. L. Yuen

INTRODUCTION

MATERIALS AND METHODS

Problematic Internet use

Psychological distress

Needs satisfaction

DATA ANALYSIS

DESCRIPTIVE STATISTICS

MODEL TESTING

ALTERNATIVE MODELS

DEGREE OF PROBLEMATIC INTERNET USE

LIMITATIONS

Conflict of Interest Statement

Got Slow Internet? 4 Cold, Hard Facts (And 6 Solutions to Everyday Errors)

Is the problem your connection to the internet?

Fact #1: Download speeds of about 100Mbps are sufficient

How Much Internet Speed Do I Need?

Fact #2: The weakest link in your connections slows everything down

Website errors 101

Error: Your connection is not secure or not private

How to fix a security or privacy error

What is a 404 error?

Pro tip: Perform a site search

What is a request timeout error?

How to resolve a 408 timeout error

What does “unavailable for legal reasons” mean?

What to do about a 451 error?

What is a Bad Gateway error?

What to do about a 502 error?

What is an HTTP 503 error?

What to do about a service unavailable error as a user?

Fact #3: Sometimes the problem really is your home internet

1. Research your plan’s speed

2. Find out about infrastructure updates near you

3. Compare providers near you (and see if it’s time to switch)

Fact #4: There’s more you can do to speed up your internet

Related Posts

If Your Internet Connection Is Slow, Research These Three Numbers

Related items

Our Company