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The impact of massive open online courses (moocs) on knowledge management using integrated innovation diffusion theory and the technology acceptance model.

1. Introduction

The impact of mooc use in saudi higher education, 2. theoretical background and hypotheses, 2.1. knowledge management (km), 2.1.1. knowledge sharing (ks), 2.1.2. knowledge application (kap), 2.1.3. knowledge access (ka), 2.2. technology acceptance model, 2.2.1. perceived technology fit, 2.2.2. perceived enjoyment (pe), 2.3. innovation diffusion theory (idt), 2.3.1. perceived compatibility (pc), 2.3.2. trialability (tr), 2.3.3. observability (ob), 2.4. perceived ease of use (peou), 2.5. perceived usefulness (pu), 2.6. attitude towards using moocs (atum), 3. research methodology, 3.1. participants, 3.2. measurements, 4. results and analysis, 4.1. measurement model analysis, 4.2. validity and reliability of measures model, 4.3. structural model analysis, 5. discussion and implications, limitations and future work, 6. conclusions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Alyoussef, I.Y. The Impact of Massive Open Online Courses (MOOCs) on Knowledge Management Using Integrated Innovation Diffusion Theory and the Technology Acceptance Model. Educ. Sci. 2023 , 13 , 531. https://doi.org/10.3390/educsci13060531

Alyoussef IY. The Impact of Massive Open Online Courses (MOOCs) on Knowledge Management Using Integrated Innovation Diffusion Theory and the Technology Acceptance Model. Education Sciences . 2023; 13(6):531. https://doi.org/10.3390/educsci13060531

Alyoussef, Ibrahim Youssef. 2023. "The Impact of Massive Open Online Courses (MOOCs) on Knowledge Management Using Integrated Innovation Diffusion Theory and the Technology Acceptance Model" Education Sciences 13, no. 6: 531. https://doi.org/10.3390/educsci13060531

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March 13, 2013

13 min read

Massive Open Online Courses, aka MOOCs, Transform Higher Education and Science

Science, engineering and technology courses have been in the vanguard of the massive open online course movement. These classes also are providing fodder for scientific research on learning

By M. Mitchell Waldrop & Nature magazine

When campus president Wallace Loh walked into Juan Uriagereka's office last August, he got right to the point. “We need courses for this thing — yesterday!”

Uriagereka, associate provost for faculty affairs at the University of Maryland in College Park, knew exactly what his boss meant. Campus administrators around the world had been buzzing for months about massive open online courses, or MOOCs: Internet-based teaching programs designed to handle thousands of students simultaneously, in part using the tactics of social-networking websites. To supplement video lectures, much of the learning comes from online comments, questions and discussions. Participants even mark one another's tests.

MOOCs had exploded into the academic consciousness in summer 2011, when a free artificial-intelligence course offered by Stanford University in California attracted 160,000 students from around the world — 23,000 of whom finished it. Now, Coursera in Mountain View, California — one of the three researcher-led start-up companies actively developing MOOCs — was inviting the University of Maryland to submit up to five courses for broadcast on its software platform. Loh wanted in. “He was very clear,” says Uriagereka. “We needed to be a part of this.”

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Similar conversations have been taking place at major universities around the world, as dozens — 74, at the last count — rush to sign up. Science, engineering and technology courses have been in the vanguard of the movement, but offerings in management, humanities and the arts are growing in popularity (see ' MOOCs rising' ). “In 25 years of observing higher education, I've never seen anything move this fast,” says Mitchell Stevens, a sociologist at Stanford and one of the leaders of an ongoing, campus-wide discussion series known as Education's Digital Future.

Image: Courtesy of Nature magazine

The ferment is attributable in part to MOOCs hitting at exactly the right time. Bricks-and-mortar campuses are unlikely to keep up with the demand for advanced education: according to one widely quoted calculation, the world would have to construct more than four new 30,000-student universities per week to accommodate the children who will reach enrolment age by 2025 (see go.nature.com/mjuzhu ), let alone the millions of adults looking for further education or career training. Colleges and universities are also under tremendous financial pressure, especially in the United States, where rocketing tuition fees and ever-expanding student debt have resulted in a backlash from politicians, parents and students demanding to know what their money is going towards.

When MOOCs came along, says Chris Dede, who studies educational technologies at Harvard University in Cambridge, Massachusetts, they promised to solve these problems by radically expanding the reach of existing campuses while streamlining the workload for educators — and universities seized on them as the next big thing.

There is reason to hope that this is a positive development, says Roy Pea, who heads a Stanford center that studies how people use technology. MOOCs, which have incorporated decades of research on how students learn best, could free faculty members from the drudgery of repetitive introductory lectures. What's more, they can record online students' every mouse click, an ability that promises to transform education research by generating data that could improve teaching in the future. “We can have microanalytics on every paper, every test, right down to what media each student prefers,” says Pea.

MOOC companies still face challenges, such as dealing with low course-completion rates and proving that they can make profit. And they have a lot of convincing to do among faculty members, says Uriagereka. “Some salivate and can't wait to be a part of it,” he says, noting that his university had 20 volunteers for its 5 inaugural MOOCs. “Others say, 'Wait a minute. How do we preserve quality? How do we connect with students?'”

Large-scale pedagogy MOOCs are largely a product of one corridor in the Stanford computer-science department, where the offices of Andrew Ng, Daphne Koller and Sebastian Thrun are just a few steps apart. But they are also the fruit of research dating back to at least the 1990s, when the explosive worldwide growth of the Internet inspired a multitude of efforts to exploit it for education. Campus administrators tended to regard such projects as a sideshow — the higher-education financial crunch was not quite as serious back then — so most experiments were the work of committed individuals, departments or research centers. But with the relentless advance of technologies such as broadband, social networking and smart phones, researchers' interest continued to grow.

Ng got involved in 2007 because he wanted to bring Stanford-quality teaching to “the people who would never be able to come to Stanford”, he says. Following a path blazed by the open-source software movement, and by earlier open-source education initiatives, he started a project to post online free lecture videos and handouts for ten of Stanford's most popular engineering courses. His approach was fairly crude, he admits: just record the lectures, put them online and hope for the best. But to his astonishment, strangers started coming up to him and saying, “Are you Professor Ng? I've been taking machine learning with you!” He began to grasp how far online courses could reach, and started working on a scaled-up version of his system. “When one professor can teach 50,000 people,” he says, “it alters the economics of education.”

One of the many people he talked to about his work was Koller, who began developing her own online-education system in 2009. Whereas Ng looked outwards, Koller wanted to look inwards and reform Stanford's teaching on-campus. She particularly wanted to promote 'flipping', a decade-old innovation in which students listen to lectures at home and do their 'homework' in class with their teachers, focusing on the most difficult aspects or discussing a concept's wider implications. This lets the instructors concentrate on the parts of teaching most of them enjoy — interacting with the students — and relieves them of the repetitive lecturing that they often dislike.

Koller also wanted to incorporate insights from the many studies showing that passively listening to a lecture is a terrible way to learn ( F. I. M. Craik and R. S. Lockhart J. Verb. Learn. Verb. Behav. 11, 671–684; 1972 ). Following an approach pioneered by other online developers over the previous decade, Koller broke each video into 8–10-minute segments separated by pauses in which students have to answer questions or solve a problem. The idea was to get them to think about what they had learned; the deeper their engagement, studies showed, the better their retention.

Finally, to encourage greater interaction among the students themselves, Koller took a cue from social-networking sites such as Facebook and gave her system an online discussion forum. As Ng explains, the idea was to extend what happens in a face-to-face study group: “Students sit with their best friends, they work on problems together, they critique each others' solutions — lots of pedagogical studies show that these more interactive modes of student engagement result in better student learning.”

Koller and Ng eventually realized that they could achieve both their goals — outreach and on-campus reform — by pooling their efforts. In late 2010, they started work on a software platform that would support discussion forums, video feeds and all the other basic services of an online course, so that an instructor only had to provide the content. But making social interaction work on a large scale turned out to be a research project of its own, says Ng. For example, standard online discussion forums are a fine way to bring communities together — for 100 or so users. “With 100,000 it gets more complicated,” he says. Hundreds of students might end up asking the same question. So the developers implemented a real-time search algorithm that would display related questions and potential answers before a student could finish typing. Ng and Koller also let students vote items up or down, much like on the link-sharing website Reddit, so that the most insightful questions would rise to the top rather than being lost in the chatter.

The two researchers even set the system up so that students could mark one another's homework for essay questions, which computers can't yet handle. Not only is such a system essential to scaling up learning, says Koller, but it also turns out to be a valuable learning experience. And experiments have shown that if the criteria are spelled out clearly, grades given by the students correlate strongly with those given by the teacher ( R. Robinson Am. Biol. Teach. 63, 474–480; 2001 ).

By early 2011, Ng and Koller were planning to demonstrate the platform on campus, and other faculty members were paying attention. Among them was Thrun, a robotics researcher who was splitting his time between Stanford and Google in Mountain View, where he worked on the development of driverless cars.

It was Thrun's idea to go big, using a platform of his own based in part on Ng and Koller's ideas. He says that he was scheduled to teach an artificial-intelligence course that autumn, along with Peter Norvig, Google's director of research, “and I thought it was a social responsibility to take it online, so we could reach more than the 200 students we would get at Stanford”. But even he hadn't imagined how big it would get. This was the course that registered 160,000 people from 195 countries after just one public announcement, a post to an artificial-intelligence mailing list. “It shocked everybody,” he says.

In response, Ng took Koller's machine-learning course public using their platform, while department chair Jennifer Widom did the same with a database course. Each attracted roughly 60,000 students. With those numbers, venture-capital funding quickly followed.

Thrun announced his company Udacity in January 2012. Arguing that most professors don't have a clue about how to exploit the online medium, he and his colleagues elected to develop their courses in-house, working with education experts to make the pedagogy as effective as possible.

Ng and Koller announced Coursera in April 2012, and took the opposite tack. They partnered with big-name universities — Stanford and three others, to start — and let them provide the content while Coursera provided the hosting and software platform.

Anant Agarwal, former head of the computer science and artificial-intelligence laboratory at MIT, had been experimenting with online learning for a decade, developing an electric-circuit simulation package called WebSim that tried to give online students an effective substitute for hands-on laboratory experience. In December 2011, inspired by goings on at Stanford, he launched MITx: an independent, not-for-profit company that would offer massive online courses from MIT on an open-source basis. It became edX in May 2012, when Harvard joined.

At the same time, the term MOOCs, which had been circulating quietly in educational circles since it was coined in 2008, took off. Media accounts boomed, and company principals were soon giving talks at the popular Technology, Entertainment and Design (TED) conferences and the annual meeting of the World Economic Forum in Davos, Switzerland. As Koller told one interviewer: “I can't believe my life!”

Learning curve The MOOC companies can point to plenty of success stories. For example, the 7,200 students who completed Agarwal's electric-circuits MOOC in spring 2012 included an 81-year-old man, a single mother with two children, and a 15-year-old prodigy from Mongolia who got a perfect score on the final exam. Udacity's Introduction to Computer Science MOOC, currently its most popular, has enrolled more than 270,000 students.

But MOOCs have also had some teething problems. “Many people have no idea what they're in for when they commit to put a course online,” says John Mitchell, a computer scientist and Stanford's first vice-provost of online learning. “Restructuring even one lecture into short, self-contained segments takes a fair amount of thinking.” So does coming up with good, compelling questions to engage the students between the segments. Then there is the push for high-quality production, he says. “It takes many hours to produce one hour of quality video.”

More worrisome are the MOOCs' dismal completion rates, which rarely rise above 15%. Completion has been a problem for distance learning ever since the first correspondence courses in the nineteenth century, says Dede. Only a small fraction of students have the drive and the perseverance to learn on their own, he says, and most people need help: “social support from their fellow students to help them keep going, and intellectual support from their professors and fellow students to help them figure out the material”. At the moment, says Dede, the MOOC companies' peer-to-peer communication tools don't do nearly enough to provide that kind of help. “They're just kind of hoping that people will figure out from the bottom up how to support each other,” he says.

The companies acknowledge that completion rates are a concern and that their platforms are still works in progress. “My aspiration isn't to reach the 1% of the world that is self-motivating,” says Thrun, “it's to reach the other 99%.” The companies are already working on enhanced social tools such as live video and text chat, for example.

And to observers such as David Krakauer, that is as it should be. “There are two ways to make something new,” says Krakauer, a biologist who directs the Institute for Discovery at the University of Wisconsin–Madison. “You can design something that's perfect on paper, and then try to build it. Or you can start with a system that's rubbish, experiment and build a better one with feedback. That's the Silicon Valley style — but it's also the scientific way.”

Silicon valley style A Silicon Valley sensibility permeates the three big MOOC firms. For example, they all subscribe to the open-source ideal. “Charging for content would be a tragedy,” says Ng. But they also see plenty of opportunities to make money using the 'freemium' model followed by Google and many other technology companies: give away the basic product to draw users, and then charge for premium add-ons.

One obvious add-on might be certification, says Ng. “You would get a certificate that verifies you took the course for a small fee like US$10–$30” — a potentially substantial revenue stream when enrolments are in six figures. In the future, the companies might also offer full university course credits for a fee; they are already working with accreditation agencies to arrange that.

Other possibilities include profiting from in-course mentoring services, career counseling — and charging universities for licensing. In October 2012, for example, edX licensed a circuit-theory MOOC designed by Agarwal to San Jose State University in California, where it was used as the online component of a flipped classroom experience. In return for the licensing fee, “the professors can offer the course on campus, tweak the course however they please, get access to students' grades and online activity, and all the analytics a teacher would want to see”, says Agarwal. In this particular experiment, he adds, the San Jose course's usual 40% failure rate fell to 9%.

Analytics are another example of the Silicon Valley style, potentially allowing the MOOC companies to do for education what Internet giants such as Google or Amazon have done for marketing. In Coursera's case, says Koller, the platform monitors the students' every mouse click — “quiz submissions, forum posts, when and where a student pauses a lecture video, or rewinds, or moves to 1.5 speed”.

The company is constantly using these data as feedback, says Koller, both for refining the platform's user interface and for improving the course content. If 90% of the students start stumbling over the review exercises for a certain lecture, for example, then maybe it is time to revise that lecture.

“But anything we do is just the tip of the iceberg,” says Koller. When data from individual students are multiplied by tens or hundreds of thousands of students per course, they reach a scale big enough to launch a whole new field of learning informatics — “big-data science for education”, Pea calls it.

Learning informatics could provide an unprecedented level of feedback for colleges and universities, says Stevens: “We haven't measured learning in higher education very often, very consistently or very well — ever.” Academics have endlessly studied factors that are associated with university enrolment and success, such as race, parental income and school achievement. They have also studied what happens after graduation: the higher earnings and other benefits that college confers, on average, over a lifetime.

“What we don't know is how college performs this magic,” says Stevens. “We certainly don't know the extent to which digitally mediated college experiences will deliver the same returns as a four-year residential experience.” Now, however, he and his colleagues can begin to see what education science will look like as it merges with data analytics. Instead of looking at aggregate data about students on average, for example, researchers can finally — with appropriate permissions and privacy safeguards — follow individual students throughout their university careers, measuring exactly how specific experiences and interactions affect their learning. “It's thrilling,” he says, “a huge intellectual frontier.”

What remains to be seen is how higher education will change in response to the new technology. Maybe not much, says Dede. Yes, the major universities will extend their courses beyond their own campuses; the MOOCs have already shown them that they can do so with relatively little effort and potentially large profits. But the MOOC founders' other goal — fundamental reform in on-campus teaching — is a much tougher proposition.

“Universities think of themselves as being in the university business, not the learning business,” explains Dede. That is, they mostly take their existing structures and practices as given, and look to MOOCs and other online technologies as a way to do things more cheaply. But experience with earlier innovations such as personal computing shows the limits of that approach, he says: real gains in the productivity and effectiveness of learning will not come until universities radically reshape those structures and practices to take full advantage of the technology.

No one knows exactly where that restructuring might end up. Lectures becoming a rarity, for example? Vast numbers of students getting their degrees entirely online? But the revolution has already begun, says Stevens. Major universities such as Stanford are taking the lead, “trying to integrate and embed digital learning into the fabric of the entire university” — and trying to master the new technology before it masters them.

Virtually everyone participating in this upheaval agrees on one thing. Colleges and universities will change — perhaps dramatically — but they will not disappear. “No one says that all education has to be online,” says Thrun. “Sometimes, a classroom is better.” Especially in communal endeavors such as science, “education is more than just knowledge”, says Dede. “It's abilities like leadership and collaboration, and traits like tenacity”, all of which are best learned face to face.

An unspoken irony weaves through almost every discussion about MOOCs: thanks to innovations such as flipping, online technology's most profound effect on education may be to make human interaction more important than ever. As Krakauer puts it, “what's absolutely clear is that the very large lecture hall can be completely replaced: there's no value added over watching it at home on an iPad screen with a cup of tea. But there is also no substitute for a conversation.”

This article is reproduced with permission from the magazine Nature . The article was first published on March 13, 2013.

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• v.22(7); 2020 Jul

Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

Elizabeth r blum.

1 Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden

Terese Stenfors

Per j palmgren, associated data.

Description of the analytic procedure for the qualitative synthesis.

Massive open online courses (MOOCs), as originally conceived, promised to provide educational access to anyone with an internet connection. However, the expansiveness of MOOC education has been found to be somewhat limited. Nonetheless, leading universities continue to offer MOOCs, including many in the health sciences, on a number of private platforms. Therefore, research on online education must include thorough understanding of the role of MOOCs. To date, studies on MOOC participants have focused mainly on learners’ assessment of the course. It is known that MOOCs are not reaching the universal audiences that were predicted, and much knowledge has been gained about learners’ perceptions of MOOCs. However, there is little scholarship on what learners themselves gain from participating in MOOCs.

As MOOC development persists and expands, scholars and developers should be made aware of the role of MOOCs in education by examining what these courses do offer their participants. The objective of this qualitative synthesis of a set of MOOC evaluation studies was to explore outcomes for MOOC learners, that is, how the learners themselves benefit from participating in MOOCs.

To explore MOOC learners’ outcomes, we conducted a qualitative synthesis in the form of a deductive thematic analysis, aggregating findings from 17 individual studies selected from an existing systematic review of MOOC evaluation methods. We structured our inquiry using the Kirkpatrick model, considering Kirkpatrick levels 2, 3, and 4 as potential themes in our analysis.

Our analysis identified six types of Kirkpatrick outcomes in 17 studies. Five of these outcomes (learning/general knowledge, skills, attitudes, confidence, and commitment) fit into Kirkpatrick Level 2, while Kirkpatrick Level 3 outcomes concerning behavior/application were seen in four studies. Two additional themes were identified outside of the Kirkpatrick framework: culture and identity outcomes and affective/emotional outcomes. Kirkpatrick Level 4 was not represented among the outcomes we examined.

Conclusions

Our findings point to some gains from MOOCs. While we can expect MOOCs to persist, how learners benefit from the experience of participating in MOOCs remains unclear.

Introduction

When the first massive open online course (MOOC) was offered in 2008, the MOOC format—free, online, and open to anyone with an internet connection—was touted as revolutionary for its potential to democratize access to educational opportunities due to its theoretically universal availability [ 1 - 3 ]. The earliest MOOCs used a connectivist paradigm in which the course was built from networks of online resources and relied on openness and participation from learners. These so-called cMOOCs had the potential to allow learners to participate in their own education outside the traditional, face-to-face classroom setting and to connect with learners worldwide [ 4 ]. Extended MOOCs (xMOOCs) brought the MOOC format back to a more traditional structure, with instructors determining the content while still providing “open” availability to anyone with internet access. In practice, there are limits to what this expansive availability has accomplished [ 2 , 5 ]. However, as MOOCs persist, it is useful to explore their role in education by examining what they do offer their participants.

Learning is a complex phenomenon that can be described from different perspectives. Understanding learning is about understanding not only learning processes but also the conditions that influence—and are influenced by—the learning process [ 6 ]. In this paper, learning is understood from a constructivist and social-constructivist perspective in which reality and new understanding are constructed by learners on the basis of their previous knowledge, perceptions, and experiences. Learning thus consists of contextual aspects (ie, teachers present information in a way that enables learners to construct meaning on the basis of their own experiences, with a focus on situating learning in an authentic activity); cognitive aspects (ie, recognizing individuals’ perception, memory, and meaning-making); and social aspects (ie, converging on learning as a social activity that occurs through interactions between the learner and others) [ 7 , 8 ]. This conception of learning thus reintegrates the artificial and no longer useful distinction between cMOOCs and xMOOCs [ 9 ].

A number of systematic reviews have examined MOOCs [ 4 , 10 - 17 ]. These reviews indicate that much research on MOOCs focuses on evaluating noncompletion rates and retention vs attrition; learner motivation and engagement as well as other behavioral elements, and how these relate to retention and achievement; implications of the latter for MOOC design; and learners’ own assessments of the courses [ 3 , 4 , 10 , 12 , 16 , 18 ]. Research also points to a lack of studies on learners’ own experiences and outcomes [ 3 , 4 , 10 ]; however, there are some exceptions [ 14 , 19 ]. For example, in their review, Pilli and Admiraal [ 19 ] investigated MOOC learner outcomes with the intention of informing MOOC course design. Joksimovic et al [ 14 ] argued that outcomes and learner engagement are commonly differentiated in the MOOC literature; however, their systematic review proposes an approach that reconnects the two, especially for MOOCs that do not include assessments (eg, cMOOCs as originally conceived). Joksimović et al [ 14 ] built on a model by Reschly and co-workers [ 20 ] that conceives of learning outcomes as “proximal” or “distal,” with academic, social, and affective outcomes within each; they modified this model for the “nonformal, digital educational settings” of MOOCs [ 14 ]. Despite their work on outcomes, Joksimovic and colleagues reiterated the finding that attempts to measure or evaluate the benefits to learners of participating in MOOCs have been mostly limited to date.

Another systematic review by Rowe et al [ 17 ] investigated the utility of open online courses (OOCs, including MOOCs) in health professions education. They evaluated the available research with a framework that included five “outcome” categories, including effectiveness (increase in learner knowledge), learner experiences, feasibility, pedagogy, and economics; they concluded that the available evidence neither unequivocally supports nor refutes the use of such courses. Their review was limited to the health professions; however, it highlighted the absence of rigorous research on MOOCs and the concurrent persistence of these courses. Their “effectiveness” category further highlighted the absence of research on benefits to MOOC learners, specifically in the health professions. They argued that the application of MOOCs in health professions education should be limited until a great deal more quality research is performed [ 17 ].

In their recent systematic review, Alturkistani et al [ 21 ] also added to the discourse on MOOC evaluation methods. Alturkistani et al identified three “evaluation-focused categories” among the studies they reviewed: learner-focused, teaching-focused, and MOOC-focused [ 21 ]. We approached this review as a jumping-off point to further synthesize understanding of MOOC learner outcomes. Here, we unpack the learner-focused category in [ 21 ] and, more specifically, the “learning outcomes and experience” subcategory to investigate the learner outcomes for the included MOOCs. In our study, “learner outcomes” are direct statements that describe the knowledge, skills, and attitudes that learners have demonstrated or are expected to reliably demonstrate when successfully completing a course. Learner outcomes is an understudied area that warrants further investigation, as MOOCs are a learning environment distinct from traditional classrooms and even other forms of e-learning, and they continue to be embraced as an educational modality [ 22 ].

Thus, despite their persistence, MOOCs have not lived up to the early expectation that they would allow widespread, nearly universal access to education. For example, there is consistent evidence that learners who use MOOCs, and indeed those who are more likely to complete them, are generally more educated and affluent [ 1 , 23 , 24 ]. There is also insufficient evidence that MOOCs are useful in areas such as health professions education [ 17 ]. MOOC learners are heterogeneous along numerous dimensions, including native language, prior training, age, economic status, and geographic location [ 24 ]. The heterogeneity of the expectations and goals of MOOC learners has also undoubtedly contributed to the difficulty of evaluating MOOCs and characterizing their benefits, a difficulty that is illustrated below in the heterogeneity of the studies reviewed. Thus, if MOOCs are not, in practice, democratizing education, and they have not lived up to traditional learning settings for at least some professional fields, what are they offering? In this study, we focus our attention on what learners do gain from participating in MOOCs, including but not limited to performance measures; that is, we explore how learners benefit from the experience of participating in MOOCs, including and beyond outcomes directly related to learning.

We conducted a qualitative synthesis in the form of a deductive thematic analysis, aggregating findings from individual studies, to explore MOOC learners’ outcomes. The datasets used and analyzed during the current study are available from the first author on reasonable request. To structure our inquiry, we relied on a commonly used framework for evaluating learning with applications in multiple learning and training settings: the Kirkpatrick model [ 25 ]. This model frames training on four levels: (1) reaction , (2) learning , (3) behavior , and (4) results . A more recent version [ 26 ] updates and clarifies the model, proposing that reaction includes customer satisfaction, engagement, and relevance; learning includes knowledge, skills, attitude, confidence, and commitment; and behavior refers to how the learner applies the learning “on the job.” The more recent version of the behavior level adds “processes and systems that reinforce, encourage, and reward performance of critical behaviors on the job” [ 26 ], which can be seen as catalysts for applying what has been learned. These processes and systems, which include job aids, coaching, work review, and incentive systems, are referred to in [ 26 ] as “required drivers” or factors that increase the likelihood that people will retain and apply what they have learned in a given setting, referred to as “required drivers”. Results are the targeted outcomes of the training, such as whether the results of the training are seen within an organization; the more recent version adds “leading indicators” (short-term measures that can indicate whether the results are likely to occur) [ 26 ].

The studies in the current synthesis derive from Alturkistani et al’s systematic review of MOOC evaluation methods [ 21 ]. Their review included studies from 2008 to 2018 that focused primarily on MOOC evaluation and studies that reviewed or applied MOOC evaluation methods. Both quantitative and qualitative studies were included, after a careful assessment of their methodological quality, as well as grey literature. During the last few years, the contribution of qualitative evidence has been acknowledged within research [ 27 ]. This is in line with the epistemological stance of this review. The complete search strategy and further details of the source review [ 21 ] can be found in [ 18 ]. Alturkistani et al [ 21 ] identified 3275 records; after a review procedure, the final review included 33 studies.

Specifically, Alturkistani et al’s “learning outcomes and experiences” subcategory was the basis for the current synthesis, as we looked at what learners gain from the experience of participating in MOOCs. This subcategory included 21 studies. We reviewed each paper in this category for findings that included learners’ outcomes . Each study was examined for outcomes specific to the learners themselves. We did not include measures of engagement, motivation, completion, or attrition in our analysis unless they were clearly tied to the outcomes for learners. In an additional step intended to capture all learner outcomes, we examined Multimedia Appendix 3 in Alturkistani et al’s review [ 21 ], which included all 33 studies. As a result of this review, we excluded 12 studies that did not include clear outcomes for learners ( Figure 1 ), which left 21 studies for our analysis. As the analysis proceeded, we determined that the outcomes in 6 of these 21 studies were not clear enough to include. Notably, we did include one study [ 28 ] that was not included in Alturkistani et al’s “learner outcomes and experience” category. Of the resulting 16 studies for analysis, 4 had more than one outcome. Multimedia Appendix 1 describes this procedure in detail.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram: systematic review (A) for a synthesis paper on MOOC learning outcomes (B). Modified from Alturkistani et al [ 21 ].

More specifically, in this qualitative synthesis, we performed a deductive thematic analysis [ 29 ] where the starting themes were the four Kirkpatrick levels. We extracted all outcomes from the 16 studies; we then placed these in Kirkpatrick level 2, 3, or 4. After this first coding, which was conducted by ERB, TS and PJP reviewed the results. Second, ERB further analyzed the findings in each category according to the subthemes within each Kirkpatrick level. Subsequently, the findings were discussed and subjected to adjustments until consensus among all investigators was reached. Although the aforementioned steps appear to be consecutively ordered, the process of analysis and search for patterns was in no way linear; rather, it was iterative and recursive. No software program was used to aid the analysis. The structure of our analysis allows for the possibility that the same study will have multiple outcomes and thus will appear under more than one level. Level 1 ( reaction ) in the Kirkpatrick model was not of interest to our investigation, as there is a great deal of existing research on learners’ assessment of MOOCs.

Outcomes that could not be matched with the Kirkpatrick levels were set aside for a separate inductive thematic analysis, which is presented as “Outcomes beyond Kirkpatrick.”

Our analysis resulted in six types of outcomes. These are summarized in Table 1 as framed by the levels in the Kirkpatrick model.

Outcomes of MOOC studies framed by Kirkpatrick Level 2 or Level 3.

a Includes a self-report.

b LMS: learning management system.

Kirkpatrick Level 2: Learning

Our deductive analysis showed that 15/16 (94%) of the examined studies included one or more outcomes corresponding to Kirkpatrick Level 2. Thus, the Learning theme here incorporates concepts such as knowledge, skill, attitude, confidence, and commitment. Each subtheme is presented using the identified data and illustrated with supporting quotations.

Subtheme: Knowledge

Most of the Level 2 outcomes we identified were scores or survey items that assessed knowledge in some form. For example, in their MOOC on new media in teaching and learning, Chen et al [ 30 ] reported ”learning performance” via quiz scores and a final paper, for which the participants could earn ”Excellence Awards.” Four studies in our sample took a longitudinal view of learning outcomes via a pretest/posttest design. For example, Konstan et al [ 31 ] used a longitudinal design to test knowledge of technology that predicts preferences based on previous behavior (recommender systems technology); based on precourse and postcourse test scores within their MOOC, the gains in knowledge measured at the end of the course persisted at a 5-month follow-up in most cases. Further, in a MOOC designed to prepare medical students for global health experiences, Jacquet and colleagues [ 32 ] found an increase in post-MOOC compared to pre-MOOC test scores. Next, using average quiz and homework scores, Liang et al [ 33 ] reported an increase in quiz and homework scores enhanced by participation in online activities. Cross [ 34 ] used preassessments and postassessments to track changes in knowledge on a scale from “novice” to “expert,” while Colvin et al [ 35 ] reported improved scores on postcourse versus precourse tests in introductory physics, and Mackay et al [ 36 ] saw a postcourse increase in scores on their assessment of participants’ knowledge about animal welfare.

Subtheme: Skill

We found several examples of skill outcomes, including self-assessed preparedness (readiness for online education) [ 37 ] and improvement in Spanish language pronunciation and comprehensibility measured by pre-MOOC and post-MOOC assessments [ 38 ]. Further, participants in a library and information science MOOC were asked “What did you gain most from taking part in the MOOC?” Their responses included “Students gained new technological skills through their learning experience ” [ 39 ]. Liu et al [ 40 ] found that learners gained skill through learning to “visualize data and critique infographics (and) learning visualization concepts and…tool use”; these were the most frequently cited “three things [students] had learned” in a journalism MOOC.

Subthemes: Commitment, Attitude, and Confidence

Other Level 2 outcomes were commitment, as shown through intention to apply knowledge [ 41 ]; attitude about animal welfare [ 36 ]; and confidence to treat patients, as measured in a randomized control trial study comparing a MOOC with a self-directed online learning module [ 28 ]. Additionally, Cross [ 34 ] reported that learners gained confidence with regard to applying what they had learned, and Mackness et al [ 42 ] also reported confidence to participate in various interactive learning activities:

They also gain the confidence to attend and contribute to live synchronous sessions, to openly share their work and ideas, and to cooperate and/or collaborate in social networking environments. “They shift from being consumers to producers.”

In their MOOC on Asian vernacular architecture, Lei et al [ 43 ] used a case study design to investigate learners’ postlearning experiences, asking, “How has the course influenced learners and their surrounding community?” This influence is reflected in the following learner’s experience:

It is through learning that I have gained the most confidence, in my identity and in my work. And I hope that this course would be the one of many stepping stones towards me being able to help inspire and nurture future generations….

Using a clinical trials MOOC, Milligan and Littlejohn [ 44 ] asked learners halfway through the course “to reflect on how their practice had changed as a result of the course.” Some learners had already seen an effect on their confidence and perspective: “These respondents reported a range of general benefits: that the course had given them a new perspective, had made them assured, or had helped them bring a greater criticality to their practice.” One participant stated, “I know why and why not…you have an overview, I cannot say I apply everything in my day to day work, but the fact that you feel more confident, for me, it helps a lot.” This outcome in turn intersects with Kirkpatrick Level 3, as discussed in the next section.

Kirkpatrick Level 3: Behavior: Application

Our analysis found 4/21 studies (19%) with evidence of Level 3 outcomes. Level 3 includes application via critical behaviors plus the presence of outcomes that make it more likely that people will retain and apply what they have learned in a given setting (the abovementioned catalysts for application or “required drivers”).

In addition to effects on confidence (Level 2), Milligan and Littlejohn [ 44 ] found evidence of Level 3 outcomes from their clinical trials MOOC; in answer to the same question as above (how their practice changed as a result of the course), most learners reported having already incorporated their learning. For example, the respondent quoted above also reported immediate effects: “Well, it gives me a better understanding of why I do what I do…I understand why I have to submit my protocol or a complete or total submission to authorities, how a protocol has been developed.” [ 44 ] Another respondent said, “It is much, much better, I could address all of the challenges much better and make better decisions, and actually I participate with this CRO in developing the protocol and the study documents and everything.”

Lei and colleagues [ 43 ] described effects on how the learners brought their experience back to their communities, a behavioral application which reflects Kirkpatrick Level 3. For example, one participant from an area damaged by earthquakes reflected:

This course helped me to see the significance of the collapsed houses, temples, shrines, monuments and courtyards in a different angle which otherwise I would not have been able to see…I have already started contributing my knowledge with the local community as we come together to rebuild what has been destroyed.

Cross [ 28 ] described learners’ goals, including plans to implement tools from the MOOC in their course design; some learners reported having already done so, which is another example of application of the MOOC experience. Employing a longitudinal study design, Konstan et al [ 31 ] investigated MOOC learners’ application of course content (recommender systems technology). Kirkpatrick Level 3 behaviors are evident in the participants’ reports of incorporating the systems at work, school, or in entrepreneurial settings, and some also applied the underlying algorithms in other contexts.

Kirkpatrick Level 4

In this qualitative synthesis, we did not find any data congruent with Kirkpatrick Level 4, which includes outcomes and “leading indicators.”

Outcomes Beyond Kirkpatrick

Not all of the outcomes described in the studies are congruent or align well with the Kirkpatrick framework; hence, we present these outcomes separately here. After our inductive thematic analysis, we identified two themes among these outcomes: “culture and identity” outcomes and “affective/emotional” outcomes. Culture and identity outcomes included “insights about themselves through personal reflection about their learning styles, professional practices, and the ways they view the world” [ 39 ], as well as connection to a community, whether of fellow educators [ 39 , 42 ] or those with a shared cultural heritage [ 43 ]. Affective outcomes such as “excitement” and “inspiration” are evident in [ 39 ], where learners gained “inspiration, energy, and excitement about the field.”

Principal Results

In this qualitative analysis, we explored the benefits that MOOCs in a broad range of subjects offer their participants. We synthesized the types of outcomes reported in a set of MOOC studies, including but not limited to outcomes that assess learning in some way. Using the Kirkpatrick model as a framework, the most prominent findings were that most of the MOOCs described in the included studies only had outcomes that could be categorized as Kirkpatrick Level 2. Kirkpatrick Level 3 outcomes were also represented, although these were not as common as Level 2 outcomes. We did not observe any Kirkpatrick Level 4 outcomes in the data we analyzed. If a MOOC were to aim for or result in Level 4 outcomes, we would expect to see changes at the organizational level. This might reveal itself in the form of implemented changes in policy in a health care setting after a group of managers participated in a policy MOOC, or in the case of higher education, a change in pedagogical training for educators after a MOOC was attended by several faculty members. Our complementary analysis of outcomes that did not align with Kirkpatrick yielded two additional themes.

Previous Research

Previous research has shown that students generally perform better in face-to-face courses than in online courses [ 45 ], and several of the studies in our review used comparisons between MOOC and non-MOOC learning contexts. The studies analyzed in this study did not report outcomes that were unique to MOOCs; however, they did provide insight into what MOOCs do and do not offer to participants. For example, in a randomized control trial by Hossain et al [ 28 ] comparing a self-paced online course with an online course with MOOC-based guidance and study tips, improvement in knowledge of spinal cord injury treatment as well as gains in confidence to treat were observed after both courses; however, there was no advantage in the MOOC group. Additionally, Chen and coworkers [ 30 ] found no difference in scores on assignments between an online and an onsite version of a digital media course. Colvin et al [ 35 ] compared learning gains measured in their MOOC with learning gains in traditional settings; they found evidence of learning in the MOOC, in which scores were slightly higher than typical for a comparable lecture-based course but significantly lower than those seen in other courses with an “interactive engagement” component. In a finding that appears counter to the above, Rubio [ 38 ] found that improvement in language comprehensibility was greater in a MOOC compared to a face-to-face course. Finally, in their review, Rowe et al [ 17 ] looked specifically at the effectiveness of MOOCs in health professions education; they concluded that it cannot be said that MOOCs “enhance student learning” despite the proliferation of MOOCs and the “hype” about their potential. These contradictory findings suggest that when comparing MOOCs to other learning formats, the benefits of MOOCs remain unclear.

MOOCs were also expected to foster and build social networks. However, in reality, the amount of interaction among MOOC participants is often limited, and a small proportion of learners are usually responsible for most of this interaction. This finding was reinforced by the studies we examined [ 42 , 43 , 45 ]. However, there are social elements to MOOC participation, as discussed in the Outcomes Beyond Kirkpatrick section above. Joksimović and colleagues [ 14 ] proposed a model that may be a useful framework for illuminating some of the outcomes that do not readily fit with the Kirkpatrick framework. Their model considers social outcomes (along with academic and affective outcomes) in “immediate,” “course-level,” and “postcourse” settings. Since affective and social outcomes are evident in the studies critically analyzed here, it is worthwhile to consider them as benefits to MOOC participation, which may warrant additional research in its own right; the model proposed by Joksimović and colleagues [ 14 ] may be a useful starting point.

Methodological Considerations

Using a well-known model to frame and lens our findings, in this study, we explored one understudied aspect of MOOCs that provides a view of what learners can gain from MOOCs. The richness of data using an in-depth secondary analysis of a small number of studies from a systematic review with broad subject matter, combined with frequent debriefing sessions and investigator triangulation, enhanced the credibility of the findings. We argue that qualitatively synthesizing existing data in an attempt to make sense of contextually and methodologically diverse findings is an important contribution to the scholarly literature. There are also some limitations to this study. Synthesizing both quantitative and qualitative data is a daunting task, as these data derive from very different paradigms. Thus, an important factor limiting the applicability of our findings is the problem with extracting results from eclectic and dissimilar studies, including qualitative and quantitative methods and grey literature, and attempting to contrast and compare them. The findings should thus be interpreted with due caution in light of this fact. Further, as our work builds on a previous review, we included only studies that were included therein. This may leave out some relevant studies, despite the rigorous inclusion criteria of the previous review. Finally, despite the frequent scholarly use of the Kirkpatrick framework, there are some inherent limitations to the model that also have implications for this work. It has been argued that the four-level model depicts an oversimplified view of learning and training effectiveness that does not take individual or contextual influences into account in the evaluation of the learning that occurs [ 46 ]. Thus, using the Kirkpatrick framework deductively as in this study and assorting “contextual” data into predefined themes was challenging. Further, Kirkpatrick’s model assumes that the four levels denote a causal chain in which positive reactions lead to greater learning and training, yielding greater transfer and, consequently, more positive results. While the Kirkpatrick model is vague about the causal relationships between level outcomes, it does imply that a simple causal relationship exists between the levels in the model [ 47 ]. Finally, in this study, we examined data that were not congruent with the framework but which are nonetheless important to the discussion of MOOC outcomes. For example, when considering the outcomes reported in the studies we reviewed, we chose not to include outcomes we viewed as belonging to Kirkpatrick Level 1, Reaction. This level is usually reserved for outcomes that reflect a participant’s reaction to a particular program or training. Since this may include how the participants “feel” about the program in question, Level 1 outcomes can certainly include an affective state in relation to the training. We found some outcomes that we described as “affective,” which included “feelings” such as excitement and inspiration. However, these feelings did not refer to the MOOC (training) itself. Instead, the “excitement” and “inspiration” were feelings about the subject of the MOOC as a result of the MOOC, which does not seem to us to fall clearly within Kirkpatrick Level 1. We believe that these feelings may even fall under Kirkpatrick Level 2 in the “Attitude” category; however, we made the conservative decision to separate them. Whether these feelings are part of a Kirkpatrick framework would be an interesting topic for further inquiry.

Our findings point to some gains from MOOCs, and while we can expect MOOCs to persist, how learners benefit from the experience of participating in these courses remains unclear. This is especially true when comparing MOOCs to other learning modes, as evidenced by the comparative studies included in our sample. In our study, we looked for gains or benefits to MOOC learners in all subject areas, and we used the Kirkpatrick framework to explore what learners might gain. From a diverse set of studies, we found outcomes that included changes in knowledge, skills, attitude, and confidence as well as changes in behavior, increased excitement about a subject, and effects on cultural identity as a result of MOOC participation. Thus, beyond outcomes that can be classified as “learning,” such as increased knowledge or skill, it does appear that MOOCs provide some value for participants via the gains described above.

In contrast to systematic reviews of MOOC research, we carried out a deeper qualitative analysis of a set of studies from one systematic review that looked only at MOOC evaluation methods. Thus, as an extension of Alturkistani et al [ 21 ], we sought to identify MOOC outcomes that benefit the learner. With a qualitative investigation of a subset of studies on MOOC evaluation methods, we were able to apply the Kirkpatrick framework to identify a number of types of learner outcomes. However, as others have pointed out, the absence of systematic ways of measuring the benefits to learners is evident in our synthesis, and work remains to be done to determine the role of MOOCs and what they offer to participants and to the world.

Acknowledgments

We would like to thank Abrar Alturkistani, MPH and Edward Meinart, PhD for collaboration on the EIT Health project that inspired this paper. We also thank Hanna Augustsson, PhD for her expert review of our application of the Kirkpatrick model. All conclusions are the authors’ own. This work was partially funded by EIT Health (Grant 18654).

Abbreviations

Multimedia Appendix 1

Authors' Contributions: ERB, TS, and PJP contributed to the conception, study design, data collection, analysis and interpretation, and drafting and critical revision of the manuscript. All authors approved the final version of the manuscript.

Conflicts of Interest: None declared.

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Article contents

Researching massive open online courses for language teaching and learning.

Published online by Cambridge University Press:  23 April 2021

There has been an increasing interest in open educational resources, open practices, and open learning spaces in language teaching and learning in the last decade (Comas-Quinn & Borthwick, Reference Comas-Quinn, Borthwick, Hampel and Stickler 2015 ; Gimeno-Sanz, Reference Gimeno-Sanz 2016 ) in which language massive open online courses (LMOOCs) have played a relevant role. Defined by Bárcena and Martín-Monje ( Reference Bárcena, Martín-Monje, Martín Monje and Bárcena 2014 : 1) as “dedicated Web-based online courses for second languages with unrestricted access and potentially unlimited participation”, LMOOCs have experienced an exponential growth since their appearance in 2012 (Jitpaisarnwattana, Reinders & Darasawang, Reference Jitpaisarnwattana, Reinders and Darasawang 2019 ), with an unexpected boost due to the recent pandemic, which has led language learning to be in the top 10 subjects of interest in MOOCs (Shah, Reference Shah 2020 ).

MOOCs have caused a stir in the educational landscape, blurring the distinction between formal, non-formal, and informal education, and encouraging new models of content delivery, tuition, assessment, and accreditation. They have been heralded as a serious attempt to democratise higher education, enabling students around the world access to good-quality language learning resources (see, for instance, Mazoue, Reference Mazoue 2013 ). MOOCs are both learner centred and socially oriented, and focus on online social interaction and flexible multimodal resources (Bárcena, Read, Martín-Monje & Castrillo, Reference Bárcena, Read, Martín-Monje, Castrillo, Cress and Delgado 2014 ). They eliminate potential problems such as registration fees and compulsory attendance but preserve the essential defining features of an academic course (i.e. syllabus, learning objectives, course materials, activities, teacher–student and student–student interaction, assessment, etc.). There is even the possibility of obtaining a certificate after completing the course, and in some cases participants obtain credits that are valid for further qualifications at university level.

Language MOOCs have been acknowledged to be an emergent and expanding research field (Godwin-Jones, Reference Godwin-Jones 2014 ; Martín-Monje & Bárcena, Reference Martín-Monje and Bárcena 2014 ; Panagiotidis, Reference Panagiotidis and Graziano 2019 ), but have been recently identified by Gillespie ( Reference Gillespie 2020 ) as one of the less studied areas in computer-assisted language learning (CALL). Despite this lack of published research, Gillespie notes that “EUROCALL [the European Association for Computer Assisted Language Learning] has an LMOOC Special Interest Group, so there is clearly interest in this area” (p. 135) and advises researchers to pursue this endeavour.

A systematic review of the published literature in LMOOCs (Sallam, Martín-Monje & Li, Reference Sallam, Martín-Monje and Li 2020 ) has revealed some interesting issues. The main common features of LMOOC design are (1) a robust set of communications for language learning, (2) short videos with linguistic and cultural content, and (3) assessment tools that are valid for heterogeneous cohorts. The main strengths of LMOOCs are the fact that they make quality language learning resources available for free, and that they redefine teacher and learner roles, making the learning process more student centred. As for challenges, some authors have noted that using MOOC platforms may make language learning a less enjoyable and/or productive experience, especially in encouraging oral production. In addition, the fact that language learning is skill based and entails a great deal of practice and interaction does not make it easy for learners trying to complete an LMOOC.

Although LMOOC research is an emergent field of study, with the first scholarly publications dating back to only 2014, it is an expanding one. The following research trends or directions for LMOOC research have been identified (Sallam et al ., Reference Sallam, Martín-Monje and Li 2020 ): (1) conceptualisation of LMOOCs and their distinctive features, (2) attempts to find the most suitable model for language teaching and learning beyond the xMOOC/cMOOC dichotomy, Footnote 1 (3) suitability of LMOOCs for languages for specific purposes courses, (4) focus on the learners and their motivation and experience throughout the course, (5) reflection on the new role of the teacher, (6) instructional design and how it affects participants’ learning and possible attrition, and (7) the importance of social learning in LMOOCs. The number and depth of these identified trends is an indication of the interest and opportunity in the field of LMOOCs.

This special issue aims to offer a timely snapshot of the emergent international research landscape regarding LMOOCs. We include submissions from authors based in China, Iceland, Ireland, Spain, and the United Kingdom discussing MOOCs for widely taught languages such as English and Spanish, as well as less widely taught languages (Irish and Icelandic). This geographical and linguistic diversity reflects the promise of MOOCs to support and develop language knowledge among dispersed communities of learners (Borthwick, Reference Borthwick, Plutino, Borthwick and Corradini 2020 ). The papers presented in this issue have a common theme: they all seek to understand aspects of quality in the design and delivery of MOOCs by focusing on different dimensions of the MOOC learner experience. MOOCs are inherently informal, learner centred, and learner driven: a MOOC learner decides how, when, and how much to engage with a course, and it is learner engagement and activity that breathes life into a MOOC through social and peer learning. The nature of MOOCs – and of learners’ relationships to them – reminds us of the importance of understanding MOOC participants in the same way that Ushioda ( Reference Ushioda 2011 ) argues that we should understand language learners; that is, holistically as “‘people’ (rather than types of learner) with uniquely individual personalities, histories, motives, interests and identities” (p. 204). This seems highly appropriate when investigating language learner experiences of informal, open, self-directed courses of learning. The articles in this issue address and explore this complexity and diversity in learner experience, challenging our assumptions about LMOOCs and their participants.

We open this special issue with a paper by Conchúr Mac Lochlainn, Mairéad Nic Giolla Mhichíl, and Elaine Beirne , who investigate participation and learner behaviour in an Irish language MOOC. They set the scene both for the special issue and for their research in a paper arguing for the importance of conceiving the LMOOC learner as an individual operating within a social, cultural, and personal context. Their argument arises from their study that uses mixed methods to explore typical patterns of behaviour among course participants alongside learners’ own reflections upon the reasons for their behaviour. The authors’ study reveals wide variation in learner behaviour, and this leads them to argue for the need for further detailed qualitative investigation to unearth a true picture of LMOOC learner needs. The authors’ findings challenge our acceptance of the value of MOOCs (to learners) as virtual communities of practice by revealing findings that demonstrate individual, silent engagement that is nonetheless highly satisfactory (to learners). The authors also uncover interesting insights into how older learners participated in their LMOOC, suggesting that this demographic group merits further study. This paper forcefully reminds us of the need to remember the individual behind the “LMOOC learner” and in doing so will give MOOC designers pause for reflection on possible assumptions that underpin the design of open online courses for language learning.

The paper by Kolbrún Friðriksdóttir also seeks to understand key aspects of effective LMOOC design through the lens of learner experience. In the study, she explores whether specific factors related to course content, design, and structure had an impact on learner retention and engagement in an LMOOC for learning Icelandic. She analyses technical tracking data on learner engagement with course content and matches it to responses to a learner survey in an attempt to identify the reality of participant engagement alongside participants’ perceptions of engagement and their stated motivations. She included in her study learners who had quit the course before completing – a demographic that is often difficult for researchers to reach and engage with. Her findings suggest that course design and structure do have an impact on engagement and motivation. As with our first paper, her results exemplify how the complexity of LMOOC learners’ lives often impacts on their ability to engage and learn – but she also discovers learners’ tenacity, personal dedication, and motivation to complete their language learning over time.

Zsuzsanna Bárkányi’s paper explores the links between motivation, computer-mediated communication, and open learning in a MOOC to investigate whether the online MOOC space is an environment that reduces foreign language speaking anxiety (FLSA). She suggests that one might assume that learners who are sufficiently motivated to voluntarily take an open MOOC, in an online “shielded” environment, might feel less FLSA than when learning in a face-to-face environment. Zsuzsanna’s study challenges this assumption. Her detailed study analyses a large amount of data from four Spanish language MOOCs, including pre- and post-course surveys and in-course form posts. She seeks to understand how MOOC learners’ motivations relate to FLSA, whether their self-efficacy beliefs alter over the course of their study, and whether there is a correlation between motivation, self-efficacy beliefs, and FLSA. Her findings show that anxiety levels throughout the MOOCs remained stable, even while self-efficacy beliefs improved. She concludes that FLSA is a feature of language learning in all environments and should be considered in course design and planning. This reminds us again of the importance of recognising the individual in the language learning process and also the specific nature of language learning in our CALL designs.

The nature of language learning and the needs of online language learners are ideas present in the paper by Christine Appel and Joan-Tomàs Pujolà , who address an aspect of language learning that is often challenging in open, large-scale online courses – that of enabling interactive speaking practice. They discuss the creation and subsequent iterations of an LMOOC that incorporates elements of speaking interaction practice: “eTandemMOOC”. There is much for course designers to learn from their design-based approach, which provides a solid foundation to develop a course that has since evolved in response to learner feedback, educator experience, and engagement data. What didn’t work is as useful here as what did, and their simultaneous design/research approach has enabled their course to develop and achieve sustained engagement and positive learner evaluations over time.

Listening and responding to LMOOC learners’ perspectives and impressions is at the heart of our final paper by Rong Luo and Zixuan Ye . Their paper takes on the difficult task of identifying quality criteria for LMOOCs and suggesting a framework for LMOOC designers to work with. They deliberately choose to work with learner perspectives as the basis of their quality framework, using grounded theory methods to analyse evaluative comments made by learners taking part in 10 popular English as a second language (ESL) MOOCs. The research focuses exclusively on learner perceptions of what works well in an LMOOC and gives useful insights into the aspects of course design and delivery that their participants found effective. The strong focus on learner perceptions of quality and value across a number of different ESL MOOCs suggests some common features related to technical functionality, pedagogical course and task design, and course delivery to consider in LMOOC design.

We would like to thank our authors for their contributions to this special issue. We received a large number of very high-quality submissions and unfortunately could not include them all; however, this reflects the range and quality of research currently underway in the area of LMOOCs. The papers included here inform and advance the field of LMOOC study. Each contribution points to new possible avenues of investigation, and we hope that this special issue will stimulate both the creation of more LMOOCs and further empirical and theoretical research to inform and develop this exciting, emerging field.

Acknowledgements

The guest editors wish to thank colleagues who contributed to this special issue by submitting or reviewing papers. We also extend our thanks to Sylvie Thouësny and Alex Boulton for their advice and support during the preparation of this issue.

1 cMOOCs, or connectivist MOOCs, do not have a centralised core of content but a networked one. In contrast, xMOOCs run on a single online platform and are more similar in structure to traditional online courses.

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• Volume 33, Issue 2
• Elena Martín-Monje (a1) and Kate Borthwick (a2)
• DOI: https://doi.org/10.1017/S0958344021000094

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What do students want to know while taking massive open online courses? Examining massive open online course students’ needs based on online forum discussions from the Universal Design for Learning approach

DOI: https://doi.org/10.1145/3576050.3576072 LAK 2023: LAK23: 13th International Learning Analytics and Knowledge Conference , Arlington, USA, March 2023

We identified the nine most dominant massive open online course (MOOC) students’ needs by topic modeling and qualitative analysis of forum discussion posts ( n = 3645) among students, staff, and instructors from 21 courses. We examined the implications of these needs using three main Universal Design for Learning (UDL) principles (representation, action and expression, and engagement). We then offered suggestions for what course providers can do to promote an equitable learning experience for MOOC students. The three suggestions are as follows: (1) providing tools such as a direct messaging application to encourage students’ socializing behaviors, (2) modifying course activities to promote more hands-on projects and sharing them, and (3) implementing a bidirectional channel, such as a natural language processing-based chatbot so that students can access useful information whenever they feel the need. We argue that it is critical to include minority students’ voices when examining needs in courses, and our methodology reflects this purpose. We also discuss how the UDL approach helped us recognize students’ needs, create more accessible MOOC learning experiences, and explore future research directions.

ACM Reference Format: Songhee Han∗, Hyangeun Ji, Zilu Jiang, Michael West and Min Liu. 2023. What do students want to know while taking massive open online courses?: Examining massive open online course students’ needs based on online forum discussions from the Universal Design for Learning approach. In LAK23: 13th International Learning Analytics and Knowledge Conference LAK 2023), March 13-17, 2023, Arlington, TX, USA . ACM, New York, NY, USA, 10 Pages. https://doi.org/10.1145/3576050.3576072

1 INTRODUCTION

Online forum discussions have a reputation for fostering candid dialogue among course participants, including students, instructors, and course provider staff [ 16 ]. As a result, a number of massive open online course (MOOC) researchers have analyzed online discussion forum data to gain knowledge about students’ learning. Due to the vast amount of data, current researchers usually incorporate automated natural language processing-based (NLP) analysis methods rather than manual analysis (e.g., [ 1 , 27 , 28 ]).

One of the most common reasons for using NLP to analyze discussion forum content is to assist students by understanding their needs. For this purpose, Latent Dirichlet Allocation (LDA) has been widely used in capturing salient words representative of topics, allowing researchers to make interpretations based on evidence [ 20 ]. Many studies based on LDA discovered its usefulness in developing a model that assists educators in more effectively locating relevant content (e.g., [ 2 ]), extracting topics from the course material, and classifying forum posts (e.g., [ 28 ]). However, there have been reports on its limitations regarding the vagueness of classifications, with many chaotic posts randomly assigned to unrelated categories because the LDA method does not take user context into account [ 12 , 24 , 29 ].

In response to this problem, more researchers are interested in the Bidirectional Encoder Representations from Transformers (BERT) technique. According to [ 10 ], BERT is a language model whose word embeddings consider both the left and right sides of the context. Therefore, it outperforms previous models, such as LDA, which ignores user contexts and uses a single embedding per word [ 8 ]. BERT's pre-trained models are excellent in tasks such as natural language inference and paraphrasing [ 11 ], enabling more accurate data analysis results [ 19 ]. Despite growing interest in BERT and reports of its excellence, there is little literature currently utilizing the BERT technique to examine students’ needs in MOOCs.

There is one more critical aspect of this study, given the diversity of MOOC students’ experiences and needs under investigation. MOOCs use the flexible features of affordable courses to support everyone who wants to learn with open access [ 9 , 15 ]. MOOCs attract students from all over the world with diverse backgrounds because they are free of charge, as opposed to small courses provided by accredited institutions funded by students’ tuition. When investigating their needs in this learning environment, it is critical to recognize that the researchers’ priority should be to provide strategies for an inclusive learning experience based on what this diverse student population desires. Because MOOCs stand for supporting everyone who wants to learn, we, educational researchers, strive for that, and we need a robust theoretical framework to help us to achieve this goal. To accomplish this, we used the Universal Design for Learning (UDL) approach to investigate practical implications based on student needs discovered through topic analysis using the BERT technique and qualitative analysis.

2 BACKGROUND

UDL is a framework and design discipline for creating a more accessible learning experience for all students, including those with and without disabilities [ 25 ]. From the perspective of UDL, learners’ variability is an evaluation standard for designing an equitable learning experience [ 21 ]. Therefore, incorporating UDL into MOOCs helps stakeholders realize that one of the most important missions for their students includes modifying a learning environment to accommodate a diverse student population. In this case, students are not asked to change; instead, they are given more options regarding various success strategies, learning material formats, and course participation methods (asynchronously or not). Table 1 shows UDL's principles, guidelines, and goals.

In this sense, many MOOC researchers have adopted the UDL as a course design discipline or theoretical framework. Some shared their development experience with MOOCs following the principles of UDL to reduce obstacles and maximize learning in a course [ 7 , 23 ]. Others adopted UDL to pinpoint quality and pedagogical accessibility barriers and provided a protocol to overcome the barriers [ 17 , 18 ]. Overall, these studies documented the operational power of UDL as a guideline in learning experience design and the advantages of UDL application in MOOCs, which involves increasing diversity and participation in courses. In this respect, UDL is different from accessibility in special education studies. Accessibility in special education means eliminating barriers for students with disabilities. However, UDL reflects on an inclusive approach supporting all learners to achieve their various learning goals better by removing possible barriers that align with the meaning of inclusiveness in MOOCs [ 15 ].

The UDL was primarily used in the current study to investigate the practical implications of students’ needs, as revealed by topic modeling and qualitative analysis results. The three main UDL principles (representation, action and expression, and engagement) [ 6 ] served as analytic points for obtaining implications based on the topic themes.

3 RESEARCH QUESTIONS

(1) What topics have emerged among students, staff, and instructors in the course support forums of massive open online courses?

(2) What are the practical implications of the emerged topic themes regarding the needs of students that course providers can meet in practice?

4.1 Settings and Data Collection

The forum discussion log data were collected from Moodle learning management system at an online learning center specialized in journalist professional development. Since 2012, the center has offered over 100 MOOCs, attracting students from 140 countries and territories on average per course provided in English. The center also provides MOOCs in Spanish and Portuguese.

For this study, we used three different forum discussion data sets from 21 courses offered in English between 2017 and 2022, which were available on the research site's current learning management system server. The three forum discussions were under the “Course Help” menu and were titled “Questions for the instructors,” “Student lounge (for informal conversations among students),” and “Questions about the course platform.” The courses covered data storytelling ( n = 5), AI technology ( n = 5), photography ( n = 2), video production ( n = 2), equity and ethics ( n = 2), journalist security ( n = 3), monetization ( n = 1), and product management ( n = 1) in journalism.

A total of 3645 forum discussion posts remained after eliminating one or two-word posts. After removing all system-generated codes and tags, we identified 16043 sentences with 200842 words in the dataset. The length of each post ranged from one sentence (with three words) to 163 sentences (with 2539 words). The average post length was four sentences (with 57 words).

4.2 Data Analyses

We processed the data in three stages: language detection and translation, stopword removal, and topic modeling. First, the cleaned data without codes and tags underwent language detection and translation (when the data was recorded in non-English). We utilized langdetect (detection) and google_trans_new (translation) python packages. Table 2 shows the language distribution that appeared in the posts. Second, stopword removal was primarily accomplished using the Python NLTK library. During this process, English stopwords (e.g., “a,” “of,” and “the”) were removed from the text based on the Python NLTK library. We also added more stopwords that were not in the library to remove words that were irrelevant to locating students’ needs (e.g., one, two, year, hi, please).

Third, topic modeling was employed to extract the essence of forum discussions to determine their needs. The modeling process was broken down into three steps. In step 1, we extracted different embeddings based on the context of a word using one of the BERT pre-trained models named “all-mpnet-base-v2,” which was rated as the best quality in the BERT pre-trained models documentation [ 30 ]. We used the UMAP and HDBSCAN algorithms in step 2 of clustering to keep a significant portion of the high-dimensional local structure in lower dimensionality and avoid forcing data points to clusters simultaneously [ 4 ].   In the third step of topic creation, we treated the dataset as a single document and then applied TF-IDF (c-TF-IDF). The results showed that words with higher TF-IDF scores were more important in a topic [ 14 ].

After achieving a single importance value for each word in a cluster, we took the top 20 words per topic based on their c-TF-IDF scores. Because the scores were proxies for information density, they also showed the degree of representativeness per cluster, which is a topic. We kept modeling until the assignments of the topics and the corresponding words converged. Table 3 lists the converged nine topics and their sizes (numbers of posts assigned to topics). Topic -1 represents the outlier posts cluster with no topics assigned to it. The other topic numbers were generated randomly by modeling and were given new numbers by size, from large (Topic 1) to small (Topic 9). Topics with sizes less than 60 were excluded from the analysis due to their low representativeness.

Although topic modeling can identify topics from datasets by extracting keywords and their importance within coherent topic representations, determining the exact meanings of topics require contextual information and subject domain knowledge for a better result [ 22 , 26 ]. Thus, we conducted a qualitative analysis of the topic modeling results. We qualitatively analyzed the top words of the topics inspired by [ 3 ] and [ 22 ]. We began by examining each topic's top 20 words to develop initial themes. The themes were then refined and validated using 20 randomly selected posts associated with those themes. After deciding on a theme for each topic, we randomly chose ten posts again and checked to see if they corresponded to the theme. Two researchers conducted a qualitative analysis on each topic, and each theme was determined when the two agreed unanimously. Finally, all topics and themes we determined were reviewed and validated by a course coordinator at the research site.

5.1 Nine Topics and Their Themes Representing Students’ Needs in MOOCs

Table 4 lists the nine topics determined by topic modeling in order of size, from large to small. Each topic theme represented the result of qualitative analysis and had one or two related UDL principles, as shown in the table. The most prevalent topic theme was the students’ attempt to build a Latin American journalist community while taking courses (Topic 1). The posts included in this category demonstrated students’ self-introductions, desires to meet new journalist friends in the same region, and requests for additional assistance (e.g., “Here in Brazil, we had a true battle in social media, involving fake news and WhatsApp. Would it be possible for you to explain a little how your work was at that time in Mexico?”). Some posts made it clear that the poster intended to meet new friends in the community in person (e.g., “Hey guys. Just curious, any Mexican enrolled in this course? Would love to meet offline and sure can use someone to hold me accountable”).

The second largest topic theme was the students’ course expectations and learning strategies. Students showed enthusiasm for taking the courses and help-seeking behaviors, looking for effective learning strategies in this category. A typical post in this category began with a poster expressing how much she was looking forward to learning the course materials and asking for learning strategies to catch up with others due to late enrollment (e.g., “Greetings to you all. Is it ok to join now? I will try to catch up with the rest. Any suggestions?”). In response to this request, some posts shared their ideas on what's important in taking courses (e.g., “I think selfcare is really important in learning, especially when you are in difficult situations to catch up with others. I am sure you can find time to try different ideas that others said as well and have come to some meaningful ones to help you relax”).

The third topic theme reflected the students’ interest in data processing, analysis, and visualization for use in journalism. They were looking for what kind of tools they should get and how many skills they should learn to use effectively in their work as journalists. Some posts mentioned specific tools (e.g., “I'm curious how many of us are using Microsoft Excel vs. Google Sheets vs. LibreOffice Calc vs. Zoho Sheet vs. ...??”), while others asked for general advice (e.g., “how can I tell more stories using raw data?”).

COVID-19, the most significant global social and health issue from 2020, was represented by the fourth topic theme. Some posts expressed an interest in how others report pandemic stories (e.g., “I look forward to learning a lot from the course and also hearing about how other students are covering the pandemic”), while others shared their thoughts on how to decide what information to convey in news reporting (e.g., “The Lancet medical journal is breaking away from a study published on the old medical journal The Lancet…fact-checking based on reliable source is what we must do”). This category also included sharing resources for fact-checking related to COVID-19 (e.g., “I did a quick fact check and found out that scientists do not yet know whether the COVID-19 vaccine reduces transmission of the virus for more details, you can click on this link below”).

The fifth one showed the students’ interest in audio-format journalism, such as podcasts and radio programs. Some posts in this category shared their expertise in this field (e.g., “For the past two years, I have been developing the audio strategy for the group alongside some very talented people, so I do wish to learn a lot from this course since creating podcasts and narrative news products are part of my day to day job and my passion”), while others inquired about the strategies of creating quality podcasts with appealing content (e.g., “I would like to start using audio to create tools for our social and educational projects. How can I do so?”).

The sixth topic theme concerned the students’ effort to form an African journalists group while taking the courses. Similarly to Topic 1, the posts in this category included brief self-introductions about their backgrounds and willingness to meet other African journalists (e.g., “I work as a regional editor with an online newspaper in Nigeria. It's so nice to be here, and I hope to connect with anyone in the same region”).

The seventh theme involved creating new news products to capture the attention of a larger audience. Some posts expressed their interest in developing a business plan strategy for a specific product theme (e.g., “I'm looking forward to learning to identify and come up with a plan to connect with people interested in stories of faith”), while others shared their general interest in this subject (e.g., “My interests are content life-cycle, audience growth, and engagement”).

The eighth was heavily focused on a course topic dealing with newsletter monetization strategies. Some posts asked about how to obtain new email lists (e.g., “I'm interested in learning more about how to find and purchase email lists”), while others sought solutions to their problems related to their newsletters (e.g., “Our email list consists of individuals who follow the organization's CEO and aren't as familiar with our organization's name/functions, yet. Do you suggest sending newsletters in our CEO's name to re-establish that relationship?”).

The ninth theme included posts about their course completion status (e.g., “I completed all my tasks, but the dashboard is still at 75% complete”) and the procedures for receiving course completion certificates (e.g., “I just completed the course, but I am confused regarding the Certificate of Completion menu”). Others also expressed their difficulties finding links to course materials (e.g., “Sorry, but where is the required reading in Module 2?”).

5.2 Implications of Topic Themes in Enhancing Students’ Learning Experience

Students learn in different ways and benefit from various learning formats and tools to establish new knowledge and skills while taking MOOCs. The UDL provides a variety of strategies and resources to assist course providers in meeting diverse learning needs and improving access to learning opportunities that lead to student success [ 6 ]. The topic themes that emerged in this study suggest what enhancement aspects could be considered when course providers wish to improve students’ learning experiences using the UDL approach.

First, MOOCs can offer multiple channels and platforms of engagement, taking into account students’ desire to connect with others in similar regions. As evidenced in the themes of Topics 1 and 6, students strongly urge to be more resourceful in their local contexts by creating a space to share their thoughts and collaborate. The background of themes that emerged in Topics 1 and 6 represent the students’ needs for robust regional communities (Latin America and Africa in this case) in courses based on their interest in connecting with local journalists. Topic 2 also demonstrates students’ need for more channels to engage in courses to sustain their effort and persist in the courses while working as professionals. Implementing a direct message tool or social media group within a course could assist them in diversifying the channels through which students can socialize while taking a course.

Second, Topics 3, 4, 5, 7, and 8 represent the students’ need for various ways to act and express themselves through learning activities. These topic themes addressed how to apply what they learned in the course to their practices. Students demonstrated their goal-oriented characteristics by explaining what they were determined to do with what they learned from courses in their daily tasks as journalists. As a result, they demanded to know real-world applications of course subjects from other peers and instructors. Implementing a portfolio-sharing tool and encouraging its use in course activities could meet the needs of these students.

Third, course providers can consider extending information representation methods to improve students’ understanding of course logistics. As evidenced by Topic 9, some students have difficulty locating information taught in multiple places throughout a course. The course logistics, such as how to navigate course materials and check their attendance, are already explained on various webpages in a course, on the FAQ, resource, and weekly announcement. The need presented by Topic 9 indicates that unidirectional information transfer via a webpage is inefficient; students require a bidirectional user interface to look up necessary information when needed. An NLP chatbot can help meet these students’ needs by providing prompt responses to their demands in real time if it is properly trained to respond to students’ help-seeking questions based on data representing user characteristics and use contexts [ 5 , 13 , 15 ].

6 DISCUSSION AND FUTURE WORK

This study identified the most dominant topics and themes of MOOC students’ needs by topic modeling and qualitative analysis of forum discussions among students, staff, and instructors. We examined the implications of these needs from the UDL approach in three aspects and provided suggestions for what course providers can do to promote an equitable learning experience for MOOC students. The three suggestions are as follows: (1) providing tools such as a direct messaging application to encourage students’ socializing behaviors, (2) modifying course activities to promote more hands-on projects and sharing them, and (3) implementing a bidirectional channel, such as an NLP chatbot, that students can benefit from aimful conversation to obtain useful information whenever they feel the need in courses.

There are three important aspects of this study. First, we incorporated the non-English raw data into the dataset, although all courses and activities were provided in English. We acknowledged that it is critical to examine the minority's voice and the overall students’ demands to understand the student's learning experience as a whole. Second, the methodology used in this study is replicable in exploring topics and themes that appeared in online forum discussions or a large set of open-ended survey data. By reflecting on the user context with bidirectional embedding per word powered by a BERT pre-trained model, we extracted higher-quality top words per topic compared to using other models [ 8 ]. In the following phase, we also conducted a qualitative analysis of the topic modeling results to gain practical implications of the topic and themes based on the students’ learning context. Third, we operated the UDL approach to look for implications of the topics and themes to offer suggestions for creating more accessible learning experiences for MOOC students with diverse backgrounds and demographic factors. As a result, the findings of this study have implications for how to support students regardless of their similarities or differences in variability. In this study, we propose that students be assisted in developing their unique and diverse learning paths by using a variety of learning formats and resources.

Like any other study, we also acknowledge that certain aspects could be improved in future research. First, more data types could be utilized in the future. The current study's scope is limited to data from online discussion forums. Although the data were gathered from multiple courses offered over the past few years, relying solely on one type of data to gain insight into students’ learning is less desirable than using multiple data types. For data triangulation, we intend to use not only forum discussion data but also interview, survey, and observational data in future studies. Additionally, measuring interrater reliability could be implemented for the qualitative analysis part in future studies. In this study, our very first attempt to qualitatively develop topic themes based on quantitative topic modeling left us with many lessons. We especially learned that we need to inform human raters with more guidance next time. For example, one rater had less trouble coming up with themes based on the keywords per topic due to her rich research experience with the student population, but the other had to spend much more time and effort to do so due to her lack of experience. Reflecting on the time spent unanimously agreeing on each topic theme in this study, we suggest providing a thorough guide to raters to avoid rater burnout and increase the efficiency of the coding process. Lastly, the voice of minorities, expressed in their native languages, should be thoroughly investigated by robust qualitative methods in the future. Although we included them in the dataset after translating them into English, it is debatable whether giving them the same weight as English posts is fair in the analysis. There may be implicit intentions in displaying their identities in public spaces, such as the course discussion forums, possibly expressing frustrations or discontent that could point to their needs. These students could follow courses provided in English and were aware that the course activities (including the forum discussions) were supposed to be conducted in English. However, they “chose” to use their native language in posting their opinions, such as Figure 1 . Although we did not focus on the intentions of these postings in this study, there seems to be a profound meaning and value in examining these MOOC students’ learning experiences in detail to promote a more inclusive learning environment in future studies.

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The role of Massive Open Online Courses in transforming academic education and university experience

• Published: 24 November 2023
• Volume 29 , pages 991–1011, ( 2024 )

Cite this article

• Saltanat Aubakirova 1 ,
• Maira Kozhamzharova 1 ,
• Gaukhar Akhmetova 2 ,
• Gulzhan Artykbayeva 1 ,
• Zauresh Iskakova 1 &
• Ramilya Zhayabayeva 1  

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The study aims to investigate the level of motivation and entrepreneurial competencies of students using online distance courses in entrepreneurial learning. Based on the Massive Open Online Courses (MOOCs) program, an experimental group of respondents took the course "How to Start Your Own Business" (March–May 2022), and their results were generated and compared with the control sample. After taking a 12-week course to improve entrepreneurial ability among students (in particular, paying attention to the motivational component of the study), one can note significant shifts in the internal positive motivation of experimental group students after taking this course. Thus, their percentage component increased to 43% compared to 25% initially (p = 0.3585). Simultaneously, substantial structural changes were observed in the analysis of students' entrepreneurial abilities, demonstrating improvements across nearly all assessment items. Future research directions should include studying the integration of MOOCs into academic programs and their impact not only on motivation, but also on success and long-term programs examining the impact of MOOC implementation on the post-graduation success of university students.

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This article was prepared within the framework of grant funding for scientific research of the Ministry of Education and Science of the Republic of Kazakhstan under the project IRN AR09058294 “Entrepreneurial University as a Way of transforming higher education in the Republic of Kazakhstan: transition problems.”

This article was prepared within the framework of grant funding for scientific research of the Ministry of Education and Science of the Republic of Kazakhstan under the project IRN AR09058294 “Entrepreneurial University as a Way of transforming higher educ,IRN AR09058294

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• Published: 13 March 2013

Online learning: Campus 2.0

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Massive open online courses are transforming higher education — and providing fodder for scientific research.

When campus president Wallace Loh walked into Juan Uriagereka's office last August, he got right to the point. “We need courses for this thing — yesterday!”

Uriagereka, associate provost for faculty affairs at the University of Maryland in College Park, knew exactly what his boss meant. Campus administrators around the world had been buzzing for months about massive open online courses, or MOOCs: Internet-based teaching programmes designed to handle thousands of students simultaneously, in part using the tactics of social-networking websites. To supplement video lectures, much of the learning comes from online comments, questions and discussions. Participants even mark one another's tests.

MOOCs had exploded into the academic consciousness in summer 2011, when a free artificial-intelligence course offered by Stanford University in California attracted 160,000 students from around the world — 23,000 of whom finished it. Now, Coursera in Mountain View, California — one of the three researcher-led start-up companies actively developing MOOCs — was inviting the University of Maryland to submit up to five courses for broadcast on its software platform. Loh wanted in. “He was very clear,” says Uriagereka. “We needed to be a part of this.”

Similar conversations have been taking place at major universities around the world, as dozens — 74, at the last count — rush to sign up. Science, engineering and technology courses have been in the vanguard of the movement, but offerings in management, humanities and the arts are growing in popularity (see 'MOOCs rising'). “In 25 years of observing higher education, I've never seen anything move this fast,” says Mitchell Stevens, a sociologist at Stanford and one of the leaders of an ongoing, campus-wide discussion series known as Education's Digital Future.

The ferment is attributable in part to MOOCs hitting at exactly the right time. Bricks-and-mortar campuses are unlikely to keep up with the demand for advanced education: according to one widely quoted calculation, the world would have to construct more than four new 30,000-student universities per week to accommodate the children who will reach enrolment age by 2025 (see go.nature.com/mjuzhu ), let alone the millions of adults looking for further education or career training. Colleges and universities are also under tremendous financial pressure, especially in the United States, where rocketing tuition fees and ever-expanding student debt have resulted in a backlash from politicians, parents and students demanding to know what their money is going towards.

When MOOCs came along, says Chris Dede, who studies educational technologies at Harvard University in Cambridge, Massachusetts, they promised to solve these problems by radically expanding the reach of existing campuses while streamlining the workload for educators — and universities seized on them as the next big thing.

There is reason to hope that this is a positive development, says Roy Pea, who heads a Stanford centre that studies how people use technology. MOOCs, which have incorporated decades of research on how students learn best, could free faculty members from the drudgery of repetitive introductory lectures. What's more, they can record online students' every mouse click, an ability that promises to transform education research by generating data that could improve teaching in the future. “We can have microanalytics on every paper, every test, right down to what media each student prefers,” says Pea.

MOOC companies still face challenges, such as dealing with low course-completion rates and proving that they can make profit. And they have a lot of convincing to do among faculty members, says Uriagereka. “Some salivate and can't wait to be a part of it,” he says, noting that his university had 20 volunteers for its 5 inaugural MOOCs. “Others say, 'Wait a minute. How do we preserve quality? How do we connect with students?'”

Large-scale pedagogy

MOOCs are largely a product of one corridor in the Stanford computer-science department, where the offices of Andrew Ng, Daphne Koller and Sebastian Thrun are just a few steps apart. But they are also the fruit of research dating back to at least the 1990s, when the explosive worldwide growth of the Internet inspired a multitude of efforts to exploit it for education. Campus administrators tended to regard such projects as a sideshow — the higher-education financial crunch was not quite as serious back then — so most experiments were the work of committed individuals, departments or research centres. But with the relentless advance of technologies such as broadband, social networking and smart phones, researchers' interest continued to grow.

Ng got involved in 2007 because he wanted to bring Stanford-quality teaching to “the people who would never be able to come to Stanford”, he says. Following a path blazed by the open-source software movement, and by earlier open-source education initiatives, he started a project to post online free lecture videos and handouts for ten of Stanford's most popular engineering courses. His approach was fairly crude, he admits: just record the lectures, put them online and hope for the best. But to his astonishment, strangers started coming up to him and saying, “Are you Professor Ng? I've been taking machine learning with you!” He began to grasp how far online courses could reach, and started working on a scaled-up version of his system. “When one professor can teach 50,000 people,” he says, “it alters the economics of education.”

One of the many people he talked to about his work was Koller, who began developing her own online-education system in 2009. Whereas Ng looked outwards, Koller wanted to look inwards and reform Stanford's teaching on-campus. She particularly wanted to promote 'flipping', a decade-old innovation in which students listen to lectures at home and do their 'homework' in class with their teachers, focusing on the most difficult aspects or discussing a concept's wider implications. This lets the instructors concentrate on the parts of teaching most of them enjoy — interacting with the students — and relieves them of the repetitive lecturing that they often dislike.

Koller also wanted to incorporate insights from the many studies showing that passively listening to a lecture is a terrible way to learn (F. I. M. Craik and R. S. Lockhart J. Verb. Learn. Verb. Behav. 11 , 671–684; 1972 ). Following an approach pioneered by other online developers over the previous decade, Koller broke each video into 8–10-minute segments separated by pauses in which students have to answer questions or solve a problem. The idea was to get them to think about what they had learned; the deeper their engagement, studies showed, the better their retention.

Finally, to encourage greater interaction among the students themselves, Koller took a cue from social-networking sites such as Facebook and gave her system an online discussion forum. As Ng explains, the idea was to extend what happens in a face-to-face study group: “Students sit with their best friends, they work on problems together, they critique each others' solutions — lots of pedagogical studies show that these more interactive modes of student engagement result in better student learning.”

Koller and Ng eventually realized that they could achieve both their goals — outreach and on-campus reform — by pooling their efforts. In late 2010, they started work on a software platform that would support discussion forums, video feeds and all the other basic services of an online course, so that an instructor only had to provide the content. But making social interaction work on a large scale turned out to be a research project of its own, says Ng. For example, standard online discussion forums are a fine way to bring communities together — for 100 or so users. “With 100,000 it gets more complicated,” he says. Hundreds of students might end up asking the same question. So the developers implemented a real-time search algorithm that would display related questions and potential answers before a student could finish typing. Ng and Koller also let students vote items up or down, much like on the link-sharing website Reddit, so that the most insightful questions would rise to the top rather than being lost in the chatter.

When one professor can teach 50,000 people it alters the economics of education.

The two researchers even set the system up so that students could mark one another's homework for essay questions, which computers can't yet handle. Not only is such a system essential to scaling up learning, says Koller, but it also turns out to be a valuable learning experience. And experiments have shown that if the criteria are spelled out clearly, grades given by the students correlate strongly with those given by the teacher ( R. Robinson Am. Biol. Teach. 63, 474–480; 2001 ).

By early 2011, Ng and Koller were planning to demonstrate the platform on campus, and other faculty members were paying attention. Among them was Thrun, a robotics researcher who was splitting his time between Stanford and Google in Mountain View, where he worked on the development of driverless cars.

It was Thrun's idea to go big, using a platform of his own based in part on Ng and Koller's ideas. He says that he was scheduled to teach an artificial-intelligence course that autumn, along with Peter Norvig, Google's director of research, “and I thought it was a social responsibility to take it online, so we could reach more than the 200 students we would get at Stanford”. But even he hadn't imagined how big it would get. This was the course that registered 160,000 people from 195 countries after just one public announcement, a post to an artificial-intelligence mailing list. “It shocked everybody,” he says.

In response, Ng took his machine-learning course public using the platform he and Koller had developed, while department chair Jennifer Widom did the same with a database course. Each attracted roughly 100,000 students. With those numbers, venture-capital funding quickly followed.

Thrun announced his company Udacity in January 2012. Arguing that most professors don't have a clue about how to exploit the online medium, he and his colleagues elected to develop their courses in-house, working with education experts to make the pedagogy as effective as possible.

Ng and Koller announced Coursera in April 2012, and took the opposite tack. They partnered with big-name universities — Stanford and three others, to start — and let them provide the content while Coursera provided the hosting and software platform.

Anant Agarwal, former head of the computer science and artificial-intelligence laboratory at MIT, had been experimenting with online learning for a decade, developing an electric-circuit simulation package called WebSim that tried to give online students an effective substitute for hands-on laboratory experience. In December 2011, inspired by goings on at Stanford, he launched MITx: an independent, not-for-profit company that would offer massive online courses from MIT on an open-source basis. It became edX in May 2012, when Harvard joined.

At the same time, the term MOOCs, which had been circulating quietly in educational circles since it was coined in 2008, took off. Media accounts boomed, and company principals were soon giving talks at the popular Technology, Entertainment and Design (TED) conferences and the annual meeting of the World Economic Forum in Davos, Switzerland. As Koller told one interviewer: “I can't believe my life!”

Learning curve

The MOOC companies can point to plenty of success stories. For example, the 7,200 students who completed Agarwal's electric-circuits MOOC in spring 2012 included an 81-year-old man, a single mother with two children, and a 15-year-old prodigy from Mongolia who got a perfect score on the final exam. Udacity's Introduction to Computer Science MOOC, currently its most popular, has enrolled more than 270,000 students.

270,000 Number of students enrolled in Udacity's Introduction to Computer Science MOOC 200,000 Number of US university first-years intending to major in computer science

But MOOCs have also had some teething problems. “Many people have no idea what they're in for when they commit to put a course online,” says John Mitchell, a computer scientist and Stanford's first vice-provost of online learning. “Restructuring even one lecture into short, self-contained segments takes a fair amount of thinking.” So does coming up with good, compelling questions to engage the students between the segments. Then there is the push for high-quality production, he says. “It takes many hours to produce one hour of quality video.”

More worrisome are the MOOCs' dismal completion rates, which rarely rise above 15%. Completion has been a problem for distance learning ever since the first correspondence courses in the nineteenth century, says Dede. Only a small fraction of students have the drive and the perseverance to learn on their own, he says, and most people need help: “social support from their fellow students to help them keep going, and intellectual support from their professors and fellow students to help them figure out the material”. At the moment, says Dede, the MOOC companies' peer-to-peer communication tools don't do nearly enough to provide that kind of help. “They're just kind of hoping that people will figure out from the bottom up how to support each other,” he says.

The companies acknowledge that completion rates are a concern and that their platforms are still works in progress. “My aspiration isn't to reach the 1% of the world that is self-motivating,” says Thrun, “it's to reach the other 99%.” The companies are already working on enhanced social tools such as live video and text chat, for example.

And to observers such as David Krakauer, that is as it should be. “There are two ways to make something new,” says Krakauer, a biologist who directs the Institute for Discovery at the University of Wisconsin–Madison. “You can design something that's perfect on paper, and then try to build it. Or you can start with a system that's rubbish, experiment and build a better one with feedback. That's the Silicon Valley style — but it's also the scientific way.”

Silicon valley style

A Silicon Valley sensibility permeates the three big MOOC firms. For example, they all subscribe to the open-source ideal. “Charging for content would be a tragedy,” says Ng. But they also see plenty of opportunities to make money using the 'freemium' model followed by Google and many other technology companies: give away the basic product to draw users, and then charge for premium add-ons.

One obvious add-on might be certification, says Ng. “You would get a certificate that verifies you took the course for a small fee like US$10–$30” — a potentially substantial revenue stream when enrolments are in six figures. In the future, the companies might also offer full university course credits for a fee; they are already working with accreditation agencies to arrange that.

Other possibilities include profiting from in-course mentoring services, career counselling — and charging universities for licensing. In October 2012, for example, edX licensed a circuit-theory MOOC designed by Agarwal to San Jose State University in California, where it was used as the online component of a flipped classroom experience. In return for the licensing fee, “the professors can offer the course on campus, tweak the course however they please, get access to students' grades and online activity, and all the analytics a teacher would want to see”, says Agarwal. In this particular experiment, he adds, the San Jose course's usual 40% failure rate fell to 9%.

Analytics are another example of the Silicon Valley style, potentially allowing the MOOC companies to do for education what Internet giants such as Google or Amazon have done for marketing. In Coursera's case, says Koller, the platform monitors the students' every mouse click — “quiz submissions, forum posts, when and where a student pauses a lecture video, or rewinds, or moves to 1.5 speed”.

The company is constantly using these data as feedback, says Koller, both for refining the platform's user interface and for improving the course content. If 90% of the students start stumbling over the review exercises for a certain lecture, for example, then maybe it is time to revise that lecture.

“But anything we do is just the tip of the iceberg,” says Koller. When data from individual students are multiplied by tens or hundreds of thousands of students per course, they reach a scale big enough to launch a whole new field of learning informatics — “big-data science for education”, Pea calls it.

Learning informatics could provide an unprecedented level of feedback for colleges and universities, says Stevens: “We haven't measured learning in higher education very often, very consistently or very well — ever.” Academics have endlessly studied factors that are associated with university enrolment and success, such as race, parental income and school achievement. They have also studied what happens after graduation: the higher earnings and other benefits that college confers, on average, over a lifetime.

“What we don't know is how college performs this magic,” says Stevens. “We certainly don't know the extent to which digitally mediated college experiences will deliver the same returns as a four-year residential experience.” Now, however, he and his colleagues can begin to see what education science will look like as it merges with data analytics. Instead of looking at aggregate data about students on average, for example, researchers can finally — with appropriate permissions and privacy safeguards — follow individual students throughout their university careers, measuring exactly how specific experiences and interactions affect their learning. “It's thrilling,” he says, “a huge intellectual frontier.”

What remains to be seen is how higher education will change in response to the new technology. Maybe not much, says Dede. Yes, the major universities will extend their courses beyond their own campuses; the MOOCs have already shown them that they can do so with relatively little effort and potentially large profits. But the MOOC founders' other goal — fundamental reform in on-campus teaching — is a much tougher proposition.

“Universities think of themselves as being in the university business, not the learning business,” explains Dede. That is, they mostly take their existing structures and practices as given, and look to MOOCs and other online technologies as a way to do things more cheaply. But experience with earlier innovations such as personal computing shows the limits of that approach, he says: real gains in the productivity and effectiveness of learning will not come until universities radically reshape those structures and practices to take full advantage of the technology.

No one knows exactly where that restructuring might end up. Lectures becoming a rarity, for example? Vast numbers of students getting their degrees entirely online? But the revolution has already begun, says Stevens. Major universities such as Stanford are taking the lead, “trying to integrate and embed digital learning into the fabric of the entire university” — and trying to master the new technology before it masters them.

Virtually everyone participating in this upheaval agrees on one thing. Colleges and universities will change — perhaps dramatically — but they will not disappear. “No one says that all education has to be online,” says Thrun. “Sometimes, a classroom is better.” Especially in communal endeavours such as science, “education is more than just knowledge”, says Dede. “It's abilities like leadership and collaboration, and traits like tenacity”, all of which are best learned face to face.

An unspoken irony weaves through almost every discussion about MOOCs: thanks to innovations such as flipping, online technology's most profound effect on education may be to make human interaction more important than ever. As Krakauer puts it, “what's absolutely clear is that the very large lecture hall can be completely replaced: there's no value added over watching it at home on an iPad screen with a cup of tea. But there is also no substitute for a conversation.”

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20 march 2013.

This article wrongly said that Andrew Ng took Daphne Koller’s machine-learning course public — it was his own course. Both this course and a database course each attracted 100,000 students not 60,000 as originally stated. The text has been corrected to reflect this.

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Waldrop, M. Online learning: Campus 2.0. Nature 495 , 160–163 (2013). https://doi.org/10.1038/495160a

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Iniesto, Francisco (2020). An Investigation Into The Accessibility Of Massive Open Online Courses (MOOCs). PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.0001117a

Massive Open Online Courses (MOOCs) are an evolution of open online learning that enables people to study online and for little or no cost. MOOCs can provide learners with the flexibility to learn, opportunities for social learning, and the chance to gain new skills and knowledge. While MOOCs have the potential to also bring these benefits to disabled learners, there is little understanding of how accessibility is embedded in the creation of MOOCs. The goal of this research has been to understand the accessibility barriers in MOOCs and to develop processes to identify and address those barriers. In the extant literature, the expectations of disabled learners when they take up MOOCs are not discussed and studies on MOOCs that report demographic data of learners do not consider disabled learners. However, disabled learners can face difficulties in accessing MOOCs, and certain learning designs of MOOCs may affect their engagement, causing them to miss out on opportunities offered by MOOCs. Technologies and the learning design approaches for MOOCs need to be as accessible as possible, so that learners can use MOOCs in a range of contexts, including via assistive technologies. This research has investigated the current state of accessibility in MOOCs. It has involved the following: Interviews with 26 MOOC providers; including software developers, accessibility managers, inclusion designers, instructional designers, course editors and learning media developers; Comparative quantitative survey data involving disabled and non-disabled learners participating in 14 MOOCs; Interviews with 15 disabled learners which have captured their experiences; and An accessibility audit was devised and then used to evaluate MOOCs from 4 major platforms: FutureLearn, edX, Coursera and Canvas. This audit comprises 4 components: technical accessibility, user experience (UX), quality and learning design; 10 experts were involved in its design and validation. This research programme has yielded an understanding of how MOOC providers cater for disabled learners, the motivations of disabled learners when taking part in MOOCs, and how MOOCs should be designed to be accessible for disabled learners. A range of barriers to accessibility in MOOCs have been identified, and an accessibility audit for MOOCs has been proposed. An open online learning environment should take into account learners’ abilities, learning goals, where learning takes place, and the different devices learners use. The research outcomes will be beneficial to MOOC providers to support the accessible design of MOOCs, including the educational resources and the platforms where the MOOCs are hosted. The ultimate beneficiaries of this research project are MOOC learners because accessible MOOCs will help support their lifelong learning and provide re-skilling opportunities.

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TDR launches new implementation research training courses

Responding to feedback from the TDR community of learners and the growing demand for more specialized training in implementation research (IR),   TDR has launched a new series of online lectures and training modules to complement its suite of existing training materials on IR.

Foundations of implementation research is a free, self-paced online lecture series designed for researchers, public health practitioners, and stakeholders involved in implementing proven interventions in low- and middle-income countries. The lectures comprise a standardized curriculum that has been co-developed by eight universities in low- and middle-income countries affiliated with TDR’s research capacity strengthening programmes.  

The 15 lectures cover topics such as addressing barriers to implementation, measurement of IR outcomes, research for scaling up interventions and translating results from IR into policy and action.  

In addition, new modules to complement TDR’s Massive Open Online Course (MOOC) on IR , available in four languages, have been launched to show implementation research in action through case studies covering specialized topics, as requested by users. Through the MOOC, TDR has already trained thousands of participants on methods for identifying and overcoming barriers to effective health intervention implementation. The newly added modules are designed to deepen participants' understanding and build skills in additional areas of IR while showcasing successful IR projects for infectious diseases. 

The new modules cover the following topics: 

• Chagas disease in rural Ecuador : This case study highlights the collaborative efforts between researchers and local communities to combat Chagas disease, showcasing innovative strategies and community-driven solutions. 
• Trachoma elimination in Ethiopia : A deep-dive into the comprehensive efforts to combat trachoma in Africa, featuring perspectives from researchers, health workers, and affected communities. 
• COVID-19 vaccine implementation in Ghana : This module presents adaptive strategies employed during the COVID-19 vaccine rollout in Ghana, highlighting the importance of community buy-in and programme responsiveness in public health crises. 
• Social innovation and community engagement (coming soon): Two specialized modules are designed to foster innovation and strengthen community partnerships within implementation research, both of which are crucial for embedding sustainable health solutions.
• IR short course for health programme implementers (coming soon): Tailored to health professionals, this course provides essential skills for collaborating with IR teams and addressing implementation challenges effectively. 

These new courses add to existing MOOC modules such as Incorporating an intersectional gender perspective in IR.  

Visit TDRMOOC.org to access the new self-paced modules and subscribe to our newsletter for scientists to get more information on when to register for guided MOOC courses. 

“These new training courses are part of efforts to strategically address the global health challenges as outlined in the new TDR Strategy 2024-2029 , by enhancing capacity for implementation research where it is most needed, working in equitable partnership with stakeholders,” said Dr Anna Thorson, head of TDR’s research capacity strengthening unit.  

For more information, please contact Dr Anna Thorson  for the MOOC and  Dr Mahnaz Vahedi  for the Postgraduate Training Scheme.

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TDR, the Special Programme for Research and Training in Tropical Diseases, is a global programme of scientific collaboration co-sponsored by the United Nations Children’s Fund (UNICEF), the United Nations Development Programme (UNDP), the World Bank and the World Health Organization (WHO).