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Response Times Reconstructor Based on Mathematical Expectation Quotient for a High Priority Task over RT-Linux

Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases as the priority of the tasks increases; however, the minimum and maximum response times are still present in the same task, and this complicates its monitoring, decreasing its level of predictability in case of contingency or overload, as well as making resource sizing difficult. Therefore, the need arises to propose a model capable of reconstructing the dynamics of response times for the instances of a task with high priority in order to analyze their offline behavior under specific working conditions. For this purpose, we develop the necessary theory to build the response time reconstruction model. Then, to test the proposed model, we set up a workbench consisting of a single board computer, PREEMPT_RT, and a high priority task generated by the execution of a matrix inversion algorithm. This work demonstrates the application of the theory in an experimental process, presenting a way to model and reconstruct the dynamics of response times by a high-priority task on RT-Linux.

Using Screen-Capture Technology to Authenticate IT Online Learning and Assessment

Assessment has always been fundamental to teaching as it aims to gauge the impact of the teaching on students learning. The current assessment in teaching computer literacy is objective assessment that focuses on making it intentional, informative, and formalized. Although this assessment is the best way when assessing large groups of students at one limited time, it has a drawback of being limited to check the knowledge of understanding terminology and recalling steps of a particular process. This study introduces a screen-capture technology based approach to performative and authentic assessment. It involved design and implementation of screen-capture assignments to assess computer maintenance skills. The sample consisted of 28 students enrolled in computer hardware and software maintenance classes. Data from students was collected through a multi-modal student survey and a semi-structured interview. The results analysis has indicated that screen-capture performative assessment promotes students’ engagement and learning level of solving real-world problems.

Obowiązek rejestracji prasy a postęp technologiczny

The aim of the article is to show the evolution of the requirements related to publishing the press and to define the directions of new legal changes. The current regulations are inadequate to the contemporary realities of the media market and communication possibilities. The obligation to register the press can be seen as a relaxed follow-up to the authoritarian or totalitarian regimes’ requirement to obtain a license to publish a journal or a periodical. Press registration would be a democratic alternative to obtaining a press license only if certain values supported it, including the interests of other persons and entities. Currently, such interests are secured by other regulations. The considerations of the courts and legal science focus on the possible contradiction of the current regulation on the registration of newspapers and magazines with the constitutional ban on licensing the press. However, it should be taken into account to a greater extent that the dissemination of the internet and computer hardware has made it more complicated to register a periodical than to start a simple press activity. Therefore, the obligation to register the press in its present form is unreasonable.

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.

Research on the Practical Teaching System Reform of Computer Hardware Courses

This study analyzes the current situation of the practical teaching system of computer hardware courses in local undergraduate colleges, scrutinizes the experimental environment construction, contents, and methods of computer hardware courses, and proposes a new practical teaching system for computer hardware courses, so as to meet the needs of transformation and development as well as application-oriented talent training.

Immersive virtual-reality computer-assembly serious game to enhance autonomous learning

AbstractImmersive virtual reality (VR) environments create a very strong sense of presence and immersion. Nowadays, especially when student isolation and online autonomous learning is required, such sensations can provide higher satisfaction and learning rates than conventional teaching. However, up until the present, learning outcomes with VR tools have yet to prove their advantageous aspects over conventional teaching. The project presents a VR serious game for teaching concepts associated with computer hardware assembly. These concepts are often included in any undergraduate’s introduction to Computer Science. The learning outcomes are evaluated using a pre-test of previous knowledge, a satisfaction/usability test, and a post-test on knowledge acquisition, structured with questions on different knowledge areas. The results of the VR serious game are compared with another two learning methodologies adapted to online learning: (1) an online conventional lecture; and (2) playing the same serious game on a desktop PC. An extensive sample of students (n = 77) was formed for this purpose. The results showed the strong potential of VR serious games to improve student well-being during spells of confinement, due to higher learning satisfaction. Besides, ease of usability and the use of in-game tutorials are directly related with game-user satisfaction and performance. The main novelty of this research is related to academic performance. Although a very limited effect was noted for learning theoretical knowledge with the VR application in comparison with the other methodologies, this effect was significantly improved through visual knowledge, understanding and making connections between different concepts. It can therefore be concluded that the proposed VR serious game has the potential to increase student learning and therefore student satisfaction, by imparting a deeper understanding of the subject matter to students.

Gambaran Keseimbangan Pada Pasien Post Stroke Setelah Pemberian Latihan Berbasis Virtual Reality: Literature Review

AbstractPost-stroke is a condition where the stroke patient has gone through an emergency so that he is in a stable condition. Post-stroke patients can experience various functional limitations, one of which is balance disorders. Patients experiencing this type of disorder can be given balance exercises based on virtual reality. Virtual reality will provide visual, proprioceptive, and auditory stimulation trough computer hardware and software to engage in artificial environments that appear and feel similar to real world objects and events. This study aimed to describe balance of post-stroke patients after being given virtual reality-based exercises. The method used in this study was a literature review analysis with the PICO method. Five articles were obtained to be reviewed from several data bases such as PubMed (n=2) and Google Scholar (n=3). The results of the analysis of the five articles showed that the average age of the respondents was > 60 years; 51.7% were female and 48.3% were male; and the average value of pre-test as well as post-test were 42.1 and 47.2 with an increase of 5.1. In conclusion, there was an increase in the balance of post-stroke patients after undergoing virtual reality-based exercises with significant results. . Therefore, researchers or practitioners are suggested to develop a Virtual Reality method on balance disorders in post-stroke patients in the form of treatment and subsequent research.Keywords: Balance; Post Stroke; Virtual Reality AbstrakPost stroke merupakan kondisi dimana pasien stroke telah melalui keadaan darurat sehingga pasien dalam keadaan stabil. Pasien post stroke dapat mengalami berbagai keterbatasan fungsional salah satunya gangguan keseimbangan yang dapat diberikan latihan keseimbangan berbasis Virtual Reality. Virtual Reality akan memberikan stimulasi visual, proprioseptif, dan pendengaran melalui perangkat keras dan perangkat lunak komputer untuk terlibat dalam lingkungan buatan yang muncul dan terasa mirip dengan objek dan peristiwa dunia nyata. Peneltian ini bertujuan untuk mengetahui gambaran keseimbangan pada pasien post stroke setelah pemberian latihan berbasis Virtual Reality. Metode yang digunakan dalam penelitian ini yaitu analisis literature review dengan metode PICO, didapatkan lima artikel untuk direview dari beberapa data base seperti PubMed (n=2) dan Google Scholar (n=3). Hasil analisis lima artikel didapatkan responden rata-rata usia > 60 tahun dan jenis kelamin perempuan 51,7% dan laki-laki 48,3%, nilai rata-rata pre test dan post test 42,1 dan 47,2 dengan peningkatan sebesar 5,1. Kesimpulannya didapatkan gambaran adanya peningkatan keseimbangan pasien post stroke setelah pemberian latihan berbasis Virtual Reality dengan hasil yang signifikan. Saran untuk peneliti atau praktisi bisa mengembangkan metode Virtual Reality pada gangguan keseimbangan pasien post stroke dalam bentuk treatmen dan penelitian berikutnya.Kata kunci: Keseimbangan; Post Stroke; Virtual Reality

Feasibility of Offering Bachelor of Technical Vocational Teacher Educa-tion (BTVTEd) Major in Computer Hardware Servicing in Sorsogon State University – Bulan Campus

Based on CHEd Memorandum Order No. 79, series of 2017, the Bachelor of Technical Vocational Teacher Education (BTVTEd) Major in Computer Hardware Servicing program is designed to enhance the knowledge, desirable values and skills of computer service technicians in accordance with industry standards. This mixed design research sought to determine the feasibility of offering this program in Sorsogon State University - Bulan Campus (SorSU-BC). It was found that the offering of the program is highly necessary and can expect a moderate sufficiency of enrolees. The program will also provide significant benefits to the different domains of the society such as the government, the community, the business industries and the students. The offering of the program is also highly sustainable in terms of enrolment, faculty, competition and facilities. It is also consistent with the vision and mission of the university and adheres to the pertinent legal foundations. Generally, faculty requirements are complied with but there is a need to hire faculty members with master’s degree in technology education or its equivalent. The laboratories and physical facilities required for the offering of the program are already available considering the existence of the IT-education and teacher-education programs. Therefore, the offering of BTVTEd major in Computer Hardware Servicing is found to be feasible. It is recommended for SorSU Bulan Campus to craft a program curriculum for BTVTEd Computer Hardware Servicing so that it can be offered in the university with the approval of the Commission on Higher Education.

How remouldable computer hardware is speeding up science

Scheduling and synchronization algorithms in operating system: a survey.

An operating system is software that is designed to manage computer hardware and software resources. However, this management requires applying an ample number of techniques and algorithms which are called synchronization and scheduling. The scheduling algorithms are used to arrange the way that the CPU is assigned to the processes, while synchronization is utilized to indicate how to work with multi-processes at the same time. Therefore, they are related to each other. CPU scheduling is a vital phenomenon of an operating system. At present, numerous CPU scheduling algorithms exist as First Come First Serve) FCFS(, Shortest Job First (SJF), Shortest Remaining Time First (SRTF), Priority Scheduling, and Round Robin (RR). In this paper, a survey of the current synchronization and scheduling algorithms have been presented. An overview of each technique with the main algorithms have been described in detail with the advantages and the issues of each algorithm. Furthermore, this paper has dug deep into the real-time operating system scheduling issues, which is the current trend in operating system researches.

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Effects of Computer-Based Training in Computer Hardware Servicing on Students' Academic Performance

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Computer-based technology and student engagement: a critical review of the literature

Laura A. Schindler ORCID: orcid.org/0000-0001-8730-5189 1 ,
Gary J. Burkholder 2 , 3 ,
Osama A. Morad 1 &
Craig Marsh 4

International Journal of Educational Technology in Higher Education volume 14 , Article number: 25 ( 2017 ) Cite this article

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Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites ( Facebook and Twitter ), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook . Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement.

Introduction

The digital revolution has profoundly affected daily living, evident in the ubiquity of mobile devices and the seamless integration of technology into common tasks such as shopping, reading, and finding directions (Anderson, 2016 ; Smith & Anderson, 2016 ; Zickuhr & Raine, 2014 ). The use of computers, mobile devices, and the Internet is at its highest level to date and expected to continue to increase as technology becomes more accessible, particularly for users in developing countries (Poushter, 2016 ). In addition, there is a growing number of people who are smartphone dependent, relying solely on smartphones for Internet access (Anderson & Horrigan, 2016 ) rather than more expensive devices such as laptops and tablets. Greater access to and demand for technology has presented unique opportunities and challenges for many industries, some of which have thrived by effectively digitizing their operations and services (e.g., finance, media) and others that have struggled to keep up with the pace of technological innovation (e.g., education, healthcare) (Gandhi, Khanna, & Ramaswamy, 2016 ).

Integrating technology into teaching and learning is not a new challenge for universities. Since the 1900s, administrators and faculty have grappled with how to effectively use technical innovations such as video and audio recordings, email, and teleconferencing to augment or replace traditional instructional delivery methods (Kaware & Sain, 2015 ; Westera, 2015 ). Within the past two decades, however, this challenge has been much more difficult due to the sheer volume of new technologies on the market. For example, in the span of 7 years (from 2008 to 2015), the number of active apps in Apple’s App Store increased from 5000 to 1.75 million. Over the next 4 years, the number of apps is projected to rise by 73%, totaling over 5 million (Nelson, 2016 ). Further compounding this challenge is the limited shelf life of new devices and software combined with significant internal organizational barriers that hinder universities from efficiently and effectively integrating new technologies (Amirault, 2012 ; Kinchin, 2012 ; Linder-VanBerschot & Summers 2015 ; Westera, 2015 ).

Many organizational barriers to technology integration arise from competing tensions between institutional policy and practice and faculty beliefs and abilities. For example, university administrators may view technology as a tool to attract and retain students, whereas faculty may struggle to determine how technology coincides with existing pedagogy (Lawrence & Lentle-Keenan, 2013 ; Lin, Singer, & Ha, 2010 ). In addition, some faculty may be hesitant to use technology due to lack of technical knowledge and/or skepticism about the efficacy of technology to improve student learning outcomes (Ashrafzadeh & Sayadian, 2015 ; Buchanan, Sainter, & Saunders, 2013 ; Hauptman, 2015 ; Johnson, 2013 ; Kidd, Davis, & Larke, 2016 ; Kopcha, Rieber, & Walker, 2016 ; Lawrence & Lentle-Keenan, 2013 ; Lewis, Fretwell, Ryan, & Parham, 2013 ; Reid, 2014 ). Organizational barriers to technology adoption are particularly problematic given the growing demands and perceived benefits among students about using technology to learn (Amirault, 2012 ; Cassidy et al., 2014 ; Gikas & Grant, 2013 ; Paul & Cochran, 2013 ). Surveys suggest that two-thirds of students use mobile devices for learning and believe that technology can help them achieve learning outcomes and better prepare them for a workforce that is increasingly dependent on technology (Chen, Seilhamer, Bennett, & Bauer, 2015 ; Dahlstrom, 2012 ). Universities that fail to effectively integrate technology into the learning experience miss opportunities to improve student outcomes and meet the expectations of a student body that has grown accustomed to the integration of technology into every facet of life (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ).

The purpose of this paper is to provide a literature review on how computer-based technology influences student engagement within higher education settings. We focused on computer-based technology given the specific types of technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) that emerged from a broad search of the literature, which is described in more detail below. Computer-based technology (hereafter referred to as technology) requires the use of specific hardware, software, and micro processing features available on a computer or mobile device. We also focused on student engagement as the dependent variable of interest because it encompasses many different aspects of the teaching and learning process (Bryson & Hand, 2007 ; Fredricks, Blumenfeld, & Parks, 1994; Wimpenny & Savin-Baden, 2013 ), compared narrower variables in the literature such as final grades or exam scores. Furthermore, student engagement has received significant attention over the past several decades due to shifts towards student-centered, constructivist instructional methods (Haggis, 2009 ; Wright, 2011 ), mounting pressures to improve teaching and learning outcomes (Axelson & Flick, 2011 ; Kuh, 2009 ), and promising studies suggesting relationships between student engagement and positive academic outcomes (Carini, Kuh, & Klein, 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). Despite the interest in student engagement and the demand for more technology in higher education, there are no articles offering a comprehensive review of how these two variables intersect. Similarly, while many existing student engagement conceptual models have expanded to include factors that influence student engagement, none highlight the overt role of technology in the engagement process (Kahu, 2013 ; Lam, Wong, Yang, & Yi, 2012 ; Nora, Barlow, & Crisp, 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ).

Our review aims to address existing gaps in the student engagement literature and seeks to determine whether student engagement models should be expanded to include technology. The review also addresses some of the organizational barriers to technology integration (e.g., faculty uncertainty and skepticism about technology) by providing a comprehensive account of the research evidence regarding how technology influences student engagement. One limitation of the literature, however, is the lack of detail regarding how teaching and learning practices were used to select and integrate technology into learning. For example, the methodology section of many studies does not include a pedagogical justification for why a particular technology was used or details about the design of the learning activity itself. Therefore, it often is unclear how teaching and learning practices may have affected student engagement levels. We revisit this issue in more detail at the end of this paper in our discussions of areas for future research and recommendations for practice. We initiated our literature review by conducting a broad search for articles published within the past 5 years, using the key words technology and higher education , in Google Scholar and the following research databases: Academic Search Complete, Communication & Mass Media Complete, Computers & Applied Sciences Complete, Education Research Complete, ERIC, PsycARTICLES, and PsycINFO . Our initial search revealed themes regarding which technologies were most prevalent in the literature (e.g., social networking, digital games), which then lead to several, more targeted searches of the same databases using specific keywords such as Facebook and student engagement. After both broad and targeted searches, we identified five technologies (web-conferencing software, blogs, wikis, social networking sites, and digital games) to include in our review.

We chose to focus on technologies for which there were multiple studies published, allowing us to identify areas of convergence and divergence in the literature and draw conclusions about positive and negative effects on student engagement. In total, we identified 69 articles relevant to our review, with 36 pertaining to social networking sites (21 for Facebook and 15 for Twitter ), 14 pertaining to digital games, seven pertaining to wikis, and six pertaining to blogs and web-conferencing software respectively. Articles were categorized according to their influence on specific types of student engagement, which will be described in more detail below. In some instances, one article pertained to multiple types of engagement. In the sections that follow, we will provide an overview of student engagement, including an explanation of common definitions and indicators of engagement, followed by a synthesis of how each type of technology influences student engagement. Finally, we will discuss areas for future research and make recommendations for practice.

Student engagement

Interest in student engagement began over 70 years ago with Ralph Tyler’s research on the relationship between time spent on coursework and learning (Axelson & Flick, 2011 ; Kuh, 2009 ). Since then, the study of student engagement has evolved and expanded considerably, through the seminal works of Pace ( 1980 ; 1984 ) and Astin ( 1984 ) about how quantity and quality of student effort affect learning and many more recent studies on the environmental conditions and individual dispositions that contribute to student engagement (Bakker, Vergel, & Kuntze, 2015 ; Gilboy, Heinerichs, & Pazzaglia, 2015 ; Martin, Goldwasser, & Galentino, 2017 ; Pellas, 2014 ). Perhaps the most well-known resource on student engagement is the National Survey of Student Engagement (NSSE), an instrument designed to assess student participation in various educational activities (Kuh, 2009 ). The NSSE and other engagement instruments like it have been used in many studies that link student engagement to positive student outcomes such as higher grades, retention, persistence, and completion (Leach, 2016 ; McClenney, Marti, & Adkins, 2012 ; Trowler & Trowler, 2010 ), further convincing universities that student engagement is an important factor in the teaching and learning process. However, despite the increased interest in student engagement, its meaning is generally not well understood or agreed upon.

Student engagement is a broad and complex phenomenon for which there are many definitions grounded in psychological, social, and/or cultural perspectives (Fredricks et al., 1994; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ). Review of definitions revealed that student engagement is defined in two ways. One set of definitions refer to student engagement as a desired outcome reflective of a student’s thoughts, feelings, and behaviors about learning. For example, Kahu ( 2013 ) defines student engagement as an “individual psychological state” that includes a student’s affect, cognition, and behavior (p. 764). Other definitions focus primarily on student behavior, suggesting that engagement is the “extent to which students are engaging in activities that higher education research has shown to be linked with high-quality learning outcomes” (Krause & Coates, 2008 , p. 493) or the “quality of effort and involvement in productive learning activities” (Kuh, 2009 , p. 6). Another set of definitions refer to student engagement as a process involving both the student and the university. For example, Trowler ( 2010 ) defined student engagement as “the interaction between the time, effort and other relevant resources invested by both students and their institutions intended to optimize the student experience and enhance the learning outcomes and development of students and the performance, and reputation of the institution” (p. 2). Similarly, the NSSE website indicates that student engagement is “the amount of time and effort students put into their studies and other educationally purposeful activities” as well as “how the institution deploys its resources and organizes the curriculum and other learning opportunities to get students to participate in activities that decades of research studies show are linked to student learning” (Center for Postsecondary Research, 2017 , para. 1).

Many existing models of student engagement reflect the latter set of definitions, depicting engagement as a complex, psychosocial process involving both student and university characteristics. Such models organize the engagement process into three areas: factors that influence student engagement (e.g., institutional culture, curriculum, and teaching practices), indicators of student engagement (e.g., interest in learning, interaction with instructors and peers, and meaningful processing of information), and outcomes of student engagement (e.g., academic achievement, retention, and personal growth) (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ). In this review, we examine the literature to determine whether technology influences student engagement. In addition, we will use Fredricks et al. ( 2004 ) typology of student engagement to organize and present research findings, which suggests that there are three types of engagement (behavioral, emotional, and cognitive). The typology is useful because it is broad in scope, encompassing different types of engagement that capture a range of student experiences, rather than narrower typologies that offer specific or prescriptive conceptualizations of student engagement. In addition, this typology is student-centered, focusing exclusively on student-focused indicators rather than combining student indicators with confounding variables, such as faculty behavior, curriculum design, and campus environment (Coates, 2008 ; Kuh, 2009 ). While such variables are important in the discussion of student engagement, perhaps as factors that may influence engagement, they are not true indicators of student engagement. Using the typology as a guide, we examined recent student engagement research, models, and measures to gain a better understanding of how behavioral, emotional, and cognitive student engagement are conceptualized and to identify specific indicators that correspond with each type of engagement, as shown in Fig. 1 .

Conceptual framework of types and indicators of student engagement

Behavioral engagement is the degree to which students are actively involved in learning activities (Fredricks et al., 2004 ; Kahu, 2013 ; Zepke, 2014 ). Indicators of behavioral engagement include time and effort spent participating in learning activities (Coates, 2008 ; Fredricks et al., 2004 ; Kahu, 2013 ; Kuh, 2009 ; Lam et al., 2012 ; Lester, 2013 ; Trowler, 2010 ) and interaction with peers, faculty, and staff (Coates, 2008 ; Kahu, 2013 ; Kuh, 2009 ; Bryson & Hand, 2007 ; Wimpenny & Savin-Baden, 2013 : Zepke & Leach, 2010 ). Indicators of behavioral engagement reflect observable student actions and most closely align with Pace ( 1980 ) and Astin’s ( 1984 ) original conceptualizations of student engagement as quantity and quality of effort towards learning. Emotional engagement is students’ affective reactions to learning (Fredricks et al., 2004 ; Lester, 2013 ; Trowler, 2010 ). Indicators of emotional engagement include attitudes, interests, and values towards learning (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ; Witkowski & Cornell, 2015 ) and a perceived sense of belonging within a learning community (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ). Emotional engagement often is assessed using self-report measures (Fredricks et al., 2004 ) and provides insight into how students feel about a particular topic, delivery method, or instructor. Finally, cognitive engagement is the degree to which students invest in learning and expend mental effort to comprehend and master content (Fredricks et al., 2004 ; Lester, 2013 ). Indicators of cognitive engagement include: motivation to learn (Lester, 2013 ; Richardson & Newby, 2006 ; Zepke & Leach, 2010 ); persistence to overcome academic challenges and meet/exceed requirements (Fredricks et al., 2004 ; Kuh, 2009 ; Trowler, 2010 ); and deep processing of information (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ; Richardson & Newby, 2006 ) through critical thinking (Coates, 2008 ; Witkowski & Cornell, 2015 ), self-regulation (e.g., set goals, plan, organize study effort, and monitor learning; Fredricks et al., 2004 ; Lester, 2013 ), and the active construction of knowledge (Coates, 2008 ; Kuh, 2009 ). While cognitive engagement includes motivational aspects, much of the literature focuses on how students use active learning and higher-order thinking, in some form, to achieve content mastery. For example, there is significant emphasis on the importance of deep learning, which involves analyzing new learning in relation previous knowledge, compared to surface learning, which is limited to memorization, recall, and rehearsal (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ).

While each type of engagement has distinct features, there is some overlap across cognitive, behavioral, and emotional domains. In instances where an indicator could correspond with more than one type of engagement, we chose to match the indicator to the type of engagement that most closely aligned, based on our review of the engagement literature and our interpretation of the indicators. Similarly, there is also some overlap among indicators. As a result, we combined and subsumed similar indicators found in the literature, where appropriate, to avoid redundancy. Achieving an in-depth understanding of student engagement and associated indicators was an important pre-cursor to our review of the technology literature. Very few articles used the term student engagement as a dependent variable given the concept is so broad and multidimensional. We found that specific indicators (e.g., interaction, sense of belonging, and knowledge construction) of student engagement were more common in the literature as dependent variables. Next, we will provide a synthesis of the findings regarding how different types of technology influence behavioral, emotional, and cognitive student engagement and associated indicators.

Influence of technology on student engagement

We identified five technologies post-literature search (i.e., web-conferencing, blogs, wikis, social networking sites , and digital games) to include in our review, based on frequency in which they appeared in the literature over the past 5 years. One commonality among these technologies is their potential value in supporting a constructivist approach to learning, characterized by the active discovery of knowledge through reflection of experiences with one’s environment, the connection of new knowledge to prior knowledge, and interaction with others (Boghossian, 2006 ; Clements, 2015 ). Another commonality is that most of the technologies, except perhaps for digital games, are designed primarily to promote interaction and collaboration with others. Our search yielded very few studies on how informational technologies, such as video lectures and podcasts, influence student engagement. Therefore, these technologies are notably absent from our review. Unlike the technologies we identified earlier, informational technologies reflect a behaviorist approach to learning in which students are passive recipients of knowledge that is transmitted from an expert (Boghossian, 2006 ). The lack of recent research on how informational technologies affect student engagement may be due to the increasing shift from instructor-centered, behaviorist approaches to student-centered, constructivist approaches within higher education (Haggis, 2009 ; Wright, 2011 ) along with the ubiquity of web 2.0 technologies.

Web-conferencing

Web-conferencing software provides a virtual meeting space where users login simultaneously and communicate about a given topic. While each software application is unique, many share similar features such as audio, video, or instant messaging options for real-time communication; screen sharing, whiteboards, and digital pens for presentations and demonstrations; polls and quizzes for gauging comprehension or eliciting feedback; and breakout rooms for small group work (Bower, 2011 ; Hudson, Knight, & Collins, 2012 ; Martin, Parker, & Deale, 2012 ; McBrien, Jones, & Cheng, 2009 ). Of the technologies included in this literature review, web-conferencing software most closely mimics the face-to-face classroom environment, providing a space where instructors and students can hear and see each other in real-time as typical classroom activities (i.e., delivering lectures, discussing course content, asking/answering questions) are carried out (Francescucci & Foster, 2013 ; Hudson et al., 2012 ). Studies on web-conferencing software deployed Adobe Connect, Cisco WebEx, Horizon Wimba, or Blackboard Collaborate and made use of multiple features, such as screen sharing, instant messaging, polling, and break out rooms. In addition, most of the studies integrated web-conferencing software into courses on a voluntary basis to supplement traditional instructional methods (Andrew, Maslin-Prothero, & Ewens, 2015 ; Armstrong & Thornton, 2012 ; Francescucci & Foster, 2013 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ). Existing studies on web-conferencing pertain to all three types of student engagement.

Studies on web-conferencing and behavioral engagement reveal mixed findings. For example, voluntary attendance in web-conferencing sessions ranged from 54 to 57% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ) and, in a comparison between a blended course with regular web-conferencing sessions and a traditional, face-to-face course, researchers found no significant difference in student attendance in courses. However, students in the blended course reported higher levels of class participation compared to students in the face-to-face course (Francescucci & Foster, 2013 ). These findings suggest while web-conferencing may not boost attendance, especially if voluntary, it may offer more opportunities for class participation, perhaps through the use of communication channels typically not available in a traditional, face-to-face course (e.g., instant messaging, anonymous polling). Studies on web-conferencing and interaction, another behavioral indicator, support this assertion. For example, researchers found that students use various features of web-conferencing software (e.g., polling, instant message, break-out rooms) to interact with peers and the instructor by asking questions, expressing opinions and ideas, sharing resources, and discussing academic content (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ).

Studies on web-conferencing and cognitive engagement are more conclusive than those for behavioral engagement, although are fewer in number. Findings suggest that students who participated in web-conferencing demonstrated critical reflection and enhanced learning through interactions with others (Armstrong & Thornton, 2012 ), higher-order thinking (e.g., problem-solving, synthesis, evaluation) in response to challenging assignments (Wdowik, 2014 ), and motivation to learn, particularly when using polling features (Hudson et al., 2012 ). There is only one study examining how web-conferencing affects emotional engagement, although it is positive suggesting that students who participated in web-conferences had higher levels of interest in course content than those who did not (Francescucci & Foster, 2013 ). One possible reason for the positive cognitive and emotional engagement findings may be that web-conferencing software provides many features that promote active learning. For example, whiteboards and breakout rooms provide opportunities for real-time, collaborative problem-solving activities and discussions. However, additional studies are needed to isolate and compare specific web-conferencing features to determine which have the greatest effect on student engagement.

A blog, which is short for Weblog, is a collection of personal journal entries, published online and presented chronologically, to which readers (or subscribers) may respond by providing additional commentary or feedback. In order to create a blog, one must compose content for an entry, which may include text, hyperlinks, graphics, audio, or video, publish the content online using a blogging application, and alert subscribers that new content is posted. Blogs may be informal and personal in nature or may serve as formal commentary in a specific genre, such as in politics or education (Coghlan et al., 2007 ). Fortunately, many blog applications are free, and many learning management systems (LMSs) offer a blogging feature that is seamlessly integrated into the online classroom. The ease of blogging has attracted attention from educators, who currently use blogs as an instructional tool for the expression of ideas, opinions, and experiences and for promoting dialogue on a wide range of academic topics (Garrity, Jones, VanderZwan, de la Rocha, & Epstein, 2014 ; Wang, 2008 ).

Studies on blogs show consistently positive findings for many of the behavioral and emotional engagement indicators. For example, students reported that blogs promoted interaction with others, through greater communication and information sharing with peers (Chu, Chan, & Tiwari, 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), and analyses of blog posts show evidence of students elaborating on one another’s ideas and sharing experiences and conceptions of course content (Sharma & Tietjen, 2016 ). Blogs also contribute to emotional engagement by providing students with opportunities to express their feelings about learning and by encouraging positive attitudes about learning (Dos & Demir, 2013 ; Chu et al., 2012 ; Yang & Chang, 2012 ). For example, Dos and Demir ( 2013 ) found that students expressed prejudices and fears about specific course topics in their blog posts. In addition, Yang and Chang ( 2012 ) found that interactive blogging, where comment features were enabled, lead to more positive attitudes about course content and peers compared to solitary blogging, where comment features were disabled.

The literature on blogs and cognitive engagement is less consistent. Some studies suggest that blogs may help students engage in active learning, problem-solving, and reflection (Chawinga, 2017 ; Chu et al., 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), while other studies suggest that students’ blog posts show very little evidence of higher-order thinking (Dos & Demir, 2013 ; Sharma & Tietjen, 2016 ). The inconsistency in findings may be due to the wording of blog instructions. Students may not necessarily demonstrate or engage in deep processing of information unless explicitly instructed to do so. Unfortunately, it is difficult to determine whether the wording of blog assignments contributed to the mixed results because many of the studies did not provide assignment details. However, studies pertaining to other technologies suggest that assignment wording that lacks specificity or requires low-level thinking can have detrimental effects on student engagement outcomes (Hou, Wang, Lin, & Chang, 2015 ; Prestridge, 2014 ). Therefore, blog assignments that are vague or require only low-level thinking may have adverse effects on cognitive engagement.

A wiki is a web page that can be edited by multiple users at once (Nakamaru, 2012 ). Wikis have gained popularity in educational settings as a viable tool for group projects where group members can work collaboratively to develop content (i.e., writings, hyperlinks, images, graphics, media) and keep track of revisions through an extensive versioning system (Roussinos & Jimoyiannis, 2013 ). Most studies on wikis pertain to behavioral engagement, with far fewer studies on cognitive engagement and none on emotional engagement. Studies pertaining to behavioral engagement reveal mixed results, with some showing very little enduring participation in wikis beyond the first few weeks of the course (Nakamaru, 2012 ; Salaber, 2014 ) and another showing active participation, as seen in high numbers of posts and edits (Roussinos & Jimoyiannis, 2013 ). The most notable difference between these studies is the presence of grading, which may account for the inconsistencies in findings. For example, in studies where participation was low, wikis were ungraded, suggesting that students may need extra motivation and encouragement to use wikis (Nakamaru, 2012 ; Salaber, 2014 ). Findings regarding the use of wikis for promoting interaction are also inconsistent. In some studies, students reported that wikis were useful for interaction, teamwork, collaboration, and group networking (Camacho, Carrión, Chayah, & Campos, 2016 ; Martínez, Medina, Albalat, & Rubió, 2013 ; Morely, 2012 ; Calabretto & Rao, 2011 ) and researchers found evidence of substantial collaboration among students (e.g., sharing ideas, opinions, and points of view) in wiki activity (Hewege & Perera, 2013 ); however, Miller, Norris, and Bookstaver ( 2012 ) found that only 58% of students reported that wikis promoted collegiality among peers. The findings in the latter study were unexpected and may be due to design flaws in the wiki assignments. For example, the authors noted that wiki assignments were not explicitly referred to in face-to-face classes; therefore, this disconnect may have prevented students from building on interactive momentum achieved during out-of-class wiki assignments (Miller et al., 2012 ).

Studies regarding cognitive engagement are limited in number but more consistent than those concerning behavioral engagement, suggesting that wikis promote high levels of knowledge construction (i.e., evaluation of arguments, the integration of multiple viewpoints, new understanding of course topics; Hewege & Perera, 2013 ), and are useful for reflection, reinforcing course content, and applying academic skills (Miller et al., 2012 ). Overall, there is mixed support for the use of wikis to promote behavioral engagement, although making wiki assignments mandatory and explicitly referring to wikis in class may help bolster participation and interaction. In addition, there is some support for using wikis to promote cognitive engagement, but additional studies are needed to confirm and expand on findings as well as explore the effect of wikis on emotional engagement.

Social networking sites

Social networking is “the practice of expanding knowledge by making connections with individuals of similar interests” (Gunawardena et al., 2009 , p. 4). Social networking sites, such as Facebook, Twitter, Instagram, and LinkedIn, allow users to create and share digital content publicly or with others to whom they are connected and communicate privately through messaging features. Two of the most popular social networking sites in the educational literature are Facebook and Twitter (Camus, Hurt, Larson, & Prevost, 2016 ; Manca & Ranieri, 2013 ), which is consistent with recent statistics suggesting that both sites also are exceedingly popular among the general population (Greenwood, Perrin, & Duggan, 2016 ). In the sections that follow, we examine how both Facebook and Twitter influence different types of student engagement.

Facebook is a web-based service that allows users to create a public or private profile and invite others to connect. Users may build social, academic, and professional connections by posting messages in various media formats (i.e., text, pictures, videos) and commenting on, liking, and reacting to others’ messages (Bowman & Akcaoglu, 2014 ; Maben, Edwards, & Malone, 2014 ; Hou et al., 2015 ). Within an educational context, Facebook has often been used as a supplementary instructional tool to lectures or LMSs to support class discussions or develop, deliver, and share academic content and resources. Many instructors have opted to create private Facebook groups, offering an added layer of security and privacy because groups are not accessible to strangers (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Clements, 2015 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 ; Shraim, 2014 ; Maben et al., 2014 ; Manca & Ranieri, 2013 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ). The majority of studies on Facebook address behavioral indicators of student engagement, with far fewer focusing on emotional or cognitive engagement.

Studies that examine the influence of Facebook on behavioral engagement focus both on participation in learning activities and interaction with peers and instructors. In most studies, Facebook activities were voluntary and participation rates ranged from 16 to 95%, with an average of rate of 47% (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Fagioli, Rios-Aguilar, & Deil-Amen, 2015 ; Rambe, 2012 ; Staines & Lauchs, 2013 ). Participation was assessed by tracking how many students joined course- or university-specific Facebook groups (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Fagioli et al., 2015 ), visited or followed course-specific Facebook pages (DiVall & Kirwin, 2012 ; Staines & Lauchs, 2013 ), or posted at least once in a course-specific Facebook page (Rambe, 2012 ). The lowest levels of participation (16%) arose from a study where community college students were invited to use the Schools App, a free application that connects students to their university’s private Facebook community. While the authors acknowledged that building an online community of college students is difficult (Fagioli et al., 2015 ), downloading the Schools App may have been a deterrent to widespread participation. In addition, use of the app was not tied to any specific courses or assignments; therefore, students may have lacked adequate incentive to use it. The highest level of participation (95%) in the literature arose from a study in which the instructor created a Facebook page where students could find or post study tips or ask questions. Followership to the page was highest around exams, when students likely had stronger motivations to access study tips and ask the instructor questions (DiVall & Kirwin, 2012 ). The wide range of participation in Facebook activities suggests that some students may be intrinsically motivated to participate, while other students may need some external encouragement. For example, Bahati ( 2015 ) found that when students assumed that a course-specific Facebook was voluntary, only 23% participated, but when the instructor confirmed that the Facebook group was, in fact, mandatory, the level of participation rose to 94%.

While voluntary participation in Facebook activities may be lower than desired or expected (Dyson, Vickers, Turtle, Cowan, & Tassone, 2015 ; Fagioli et al., 2015 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ), students seem to have a clear preference for Facebook compared to other instructional tools (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ). For example, in one study where an instructor shared course-related information in a Facebook group, in the LMS, and through email, the level of participation in the Facebook group was ten times higher than in email or the LMS (Clements, 2015 ). In other studies, class discussions held in Facebook resulted in greater levels of participation and dialogue than class discussions held in LMS discussion forums (Camus et al., 2016 ; Hurt et al., 2012 ; Kent, 2013 ). Researchers found that preference for Facebook over the university’s LMS is due to perceptions that the LMS is outdated and unorganized and reports that Facebook is more familiar, convenient, and accessible given that many students already visit the social networking site multiple times per day (Clements, 2015 ; Dougherty & Andercheck, 2014 ; Hurt et al., 2012 ; Kent, 2013 ). In addition, students report that Facebook helps them stay engaged in learning through collaboration and interaction with both peers and instructors (Bahati, 2015 ; Shraim, 2014 ), which is evident in Facebook posts where students collaborated to study for exams, consulted on technical and theoretical problem solving, discussed course content, exchanged learning resources, and expressed opinions as well as academic successes and challenges (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 Ivala & Gachago, 2012 ; Maben et al., 2014 ; Rambe, 2012 ; van Beynen & Swenson, 2016 ).

There is far less evidence in the literature about the use of Facebook for emotional and cognitive engagement. In terms of emotional engagement, studies suggest that students feel positively about being part of a course-specific Facebook group and that Facebook is useful for expressing feelings about learning and concerns for peers, through features such as the “like” button and emoticons (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Naghdipour & Eldridge, 2016 ). In addition, being involved in a course-specific Facebook group was positively related to students’ sense of belonging in the course (Dougherty & Andercheck, 2014 ). The research on cognitive engagement is less conclusive, with some studies suggesting that Facebook participation is related to academic persistence (Fagioli et al., 2015 ) and self-regulation (Dougherty & Andercheck, 2014 ) while other studies show low levels of knowledge construction in Facebook posts (Hou et al., 2015 ), particularly when compared to discussions held in the LMS. One possible reason may be because the LMS is associated with formal, academic interactions while Facebook is associated with informal, social interactions (Camus et al., 2016 ). While additional research is needed to confirm the efficacy of Facebook for promoting cognitive engagement, studies suggest that Facebook may be a viable tool for increasing specific behavioral and emotional engagement indicators, such as interactions with others and a sense of belonging within a learning community.

Twitter is a web-based service where subscribers can post short messages, called tweets, in real-time that are no longer than 140 characters in length. Tweets may contain hyperlinks to other websites, images, graphics, and/or videos and may be tagged by topic using the hashtag symbol before the designated label (e.g., #elearning). Twitter subscribers may “follow” other users and gain access to their tweets and also may “retweet” messages that have already been posted (Hennessy, Kirkpatrick, Smith, & Border, 2016 ; Osgerby & Rush, 2015 ; Prestridge, 2014 ; West, Moore, & Barry, 2015 ; Tiernan, 2014 ;). Instructors may use Twitter to post updates about the course, clarify expectations, direct students to additional learning materials, and encourage students to discuss course content (Bista, 2015 ; Williams & Whiting, 2016 ). Several of the studies on the use of Twitter included broad, all-encompassing measures of student engagement and produced mixed findings. For example, some studies suggest that Twitter increases student engagement (Evans, 2014 ; Gagnon, 2015 ; Junco, Heibergert, & Loken, 2011 ) while other studies suggest that Twitter has little to no influence on student engagement (Junco, Elavsky, & Heiberger, 2013 ; McKay, Sanko, Shekhter, & Birnbach, 2014 ). In both studies suggesting little to no influence on student engagement, Twitter use was voluntary and in one of the studies faculty involvement in Twitter was low, which may account for the negative findings (Junco et al., 2013 ; McKay et al., 2014 ). Conversely, in the studies that show positive findings, Twitter use was mandatory and often directly integrated with required assignments (Evans, 2014 ; Gagnon, 2015 ; Junco et al., 2011 ). Therefore, making Twitter use mandatory, increasing faculty involvement in Twitter, and integrating Twitter into assignments may help to increase student engagement.

Studies pertaining to specific behavioral student engagement indicators also reveal mixed findings. For example, in studies where course-related Twitter use was voluntary, 45-91% of students reported using Twitter during the term (Hennessy et al., 2016 ; Junco et al., 2013 ; Ross, Banow, & Yu, 2015 ; Tiernan, 2014 ; Williams & Whiting, 2016 ), but only 30-36% reported making contributions to the course-specific Twitter page (Hennessy et al., 2016 ; Tiernan, 2014 ; Ross et al., 2015 ; Williams & Whiting, 2016 ). The study that reported a 91% participation rate was unique because the course-specific Twitter page was accessible via a public link. Therefore, students who chose only to view the content (58%), rather than contribute to the page, did not have to create a Twitter account (Hennessy et al., 2016 ). The convenience of not having to create an account may be one reason for much higher participation rates. In terms of low participation rates, a lack of literacy, familiarity, and interest in Twitter , as well as a preference for Facebook , are cited as contributing factors (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). However, when the use of Twitter was required and integrated into class discussions, the participation rate was 100% (Gagnon, 2015 ). Similarly, 46% of students in one study indicated that they would have been more motivated to participate in Twitter activities if they were graded (Osgerby & Rush, 2015 ), again confirming the power of extrinsic motivating factors.

Studies also show mixed results for the use of Twitter to promote interactions with peers and instructors. Researchers found that when instructors used Twitter to post updates about the course, ask and answer questions, and encourage students to tweet about course content, there was evidence of student-student and student-instructor interactions in tweets (Hennessy et al., 2016 ; Tiernan, 2014 ). Some students echoed these findings, suggesting that Twitter is useful for sharing ideas and resources, discussing course content, asking the instructor questions, and networking (Chawinga, 2017 ; Evans, 2014 ; Gagnon, 2015 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 ; West et al., 2015 ) and is preferable over speaking aloud in class because it is more comfortable, less threatening, and more concise due to the 140 character limit (Gagnon, 2015 ; Mysko & Delgaty, 2015 ; Tiernan, 2014 ). Conversely, other students reported that Twitter was not useful for improving interaction because they viewed it predominately for social, rather than academic, interactions and they found the 140 character limit to be frustrating and restrictive. A theme among the latter studies was that a large proportion of the sample had never used Twitter before (Bista, 2015 ; McKay et al., 2014 ; Osgerby & Rush, 2015 ), which may have contributed to negative perceptions.

The literature on the use of Twitter for cognitive and emotional engagement is minimal but nonetheless promising in terms of promoting knowledge gains, the practical application of content, and a sense of belonging among users. For example, using Twitter to respond to questions that arose in lectures and tweet about course content throughout the term is associated with increased understanding of course content and application of knowledge (Kim et al., 2015 ; Tiernan, 2014 ; West et al., 2015 ). While the underlying mechanisms pertaining to why Twitter promotes an understanding of content and application of knowledge are not entirely clear, Tiernan ( 2014 ) suggests that one possible reason may be that Twitter helps to break down communication barriers, encouraging shy or timid students to participate in discussions that ultimately are richer in dialogue and debate. In terms of emotional engagement, students who participated in a large, class-specific Twitter page were more likely to feel a sense of community and belonging compared to those who did not participate because they could more easily find support from and share resources with other Twitter users (Ross et al., 2015 ). Despite the positive findings about the use of Twitter for cognitive and emotional engagement, more studies are needed to confirm existing results regarding behavioral engagement and target additional engagement indicators such as motivation, persistence, and attitudes, interests, and values about learning. In addition, given the strong negative perceptions of Twitter that still exist, additional studies are needed to confirm Twitter ’s efficacy for promoting different types of behavioral engagement among both novice and experienced Twitter users, particularly when compared to more familiar tools such as Facebook or LMS discussion forums.

Digital games

Digital games are “applications using the characteristics of video and computer games to create engaging and immersive learning experiences for delivery of specified learning goals, outcomes and experiences” (de Freitas, 2006 , p. 9). Digital games often serve the dual purpose of promoting the achievement of learning outcomes while making learning fun by providing simulations of real-world scenarios as well as role play, problem-solving, and drill and repeat activities (Boyle et al., 2016 ; Connolly, Boyle, MacArthur, Hainey, & Boyle, 2012 ; Scarlet & Ampolos, 2013 ; Whitton, 2011 ). In addition, gamified elements, such as digital badges and leaderboards, may be integrated into instruction to provide additional motivation for completing assigned readings and other learning activities (Armier, Shepherd, & Skrabut, 2016 ; Hew, Huang, Chu, & Chiu, 2016 ). The pedagogical benefits of digital games are somewhat distinct from the other technologies addressed in this review, which are designed primarily for social interaction. While digital games may be played in teams or allow one player to compete against another, the focus of their design often is on providing opportunities for students to interact with academic content in a virtual environment through decision-making, problem-solving, and reward mechanisms. For example, a digital game may require students to adopt a role as CEO in a computer-simulated business environment, make decisions about a series of organizational issues, and respond to the consequences of those decisions. In this example and others, digital games use adaptive learning principles, where the learning environment is re-configured or modified in response to the actions and needs of students (Bower, 2016 ). Most of the studies on digital games focused on cognitive and emotional indicators of student engagement, in contrast to the previous technologies addressed in this review which primarily focused on behavioral indicators of engagement.

Existing studies provide support for the influence of digital games on cognitive engagement, through achieving a greater understanding of course content and demonstrating higher-order thinking skills (Beckem & Watkins, 2012 ; Farley, 2013 ; Ke, Xie, & Xie, 2016 ; Marriott, Tan, & Marriott, 2015 ), particularly when compared to traditional instructional methods, such as giving lectures or assigning textbook readings (Lu, Hallinger, & Showanasai, 2014 ; Siddique, Ling, Roberson, Xu, & Geng, 2013 ; Zimmermann, 2013 ). For example, in a study comparing courses that offered computer simulations of business challenges (e.g, implementing a new information technology system, managing a startup company, and managing a brand of medicine in a simulated market environment) and courses that did not, students in simulation-based courses reported higher levels of action-directed learning (i.e., connecting theory to practice in a business context) than students in traditional, non-simulation-based courses (Lu et al., 2014 ). Similarly, engineering students who participated in a car simulator game, which was designed to help students apply and reinforce the knowledge gained from lectures, demonstrated higher levels of critical thinking (i.e., analysis, evaluation) on a quiz than students who only attended lectures (Siddique et al., 2013 ).

Motivation is another cognitive engagement indicator that is linked to digital games (Armier et al., 2016 ; Chang & Wei, 2016 ; Dichev & Dicheva, 2017 ; Grimley, Green, Nilsen, & Thompson, 2012 ; Hew et al., 2016 ; Ibáñez, Di-Serio, & Delgado-Kloos, 2014 ; Ke et al., 2016 ; Liu, Cheng, & Huang, 2011 ; Nadolny & Halabi, 2016 ). Researchers found that incorporating gamified elements into courses, such as giving students digital rewards (e.g., redeemable points, trophies, and badges) for participating in learning activities or creating competition through the use of leaderboards where students can see how they rank against other students positively affects student motivation to complete learning tasks (Armier et al., 2016 ; Chang & Wei, 2016 ; Hew et al., 2016 ; Nadolny & Halabi, 2016 ). In addition, students who participated in gamified elements, such as trying to earn digital badges, were more motivated to complete particularly difficult learning activities (Hew et al., 2016 ) and showed persistence in exceeding learning requirements (Ibáñez et al., 2014 ). Research on emotional engagement may help to explain these findings. Studies suggest that digital games positively affect student attitudes about learning, evident in student reports that games are fun, interesting, and enjoyable (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), which may account for higher levels of student motivation in courses that offered digital games.

Research on digital games and behavioral engagement is more limited, with only one study suggesting that games lead to greater participation in educational activities (Hew et al., 2016 ). Therefore, more research is needed to explore how digital games may influence behavioral engagement. In addition, research is needed to determine whether the underlying technology associated with digital games (e.g., computer-based simulations and virtual realities) produce positive engagement outcomes or whether common mechanisms associated with both digital and non-digital games (e.g., role play, rewards, and competition) account for those outcomes. For example, studies in which non-digital, face-to-face games were used also showed positive effects on student engagement (Antunes, Pacheco, & Giovanela, 2012 ; Auman, 2011 ; Coffey, Miller, & Feuerstein, 2011 ; Crocco, Offenholley, & Hernandez, 2016 ; Poole, Kemp, Williams, & Patterson, 2014 ; Scarlet & Ampolos, 2013 ); therefore, it is unclear if and how digitizing games contributes to student engagement.

Discussion and implications

Student engagement is linked to a number of academic outcomes, such as retention, grade point average, and graduation rates (Carini et al., 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). As a result, universities have shown a strong interest in how to increase student engagement, particularly given rising external pressures to improve learning outcomes and prepare students for academic success (Axelson & Flick, 2011 ; Kuh, 2009 ). There are various models of student engagement that identify factors that influence student engagement (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ); however, none include the overt role of technology despite the growing trend and student demands to integrate technology into the learning experience (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ). Therefore, the primary purpose of our literature review was to explore whether technology influences student engagement. The secondary purpose was to address skepticism and uncertainty about pedagogical benefits of technology (Ashrafzadeh & Sayadian, 2015 ; Kopcha et al., 2016 ; Reid, 2014 ) by reviewing the literature regarding the efficacy of specific technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) for promoting student engagement and offering recommendations for effective implementation, which are included at the end of this paper. In the sections that follow, we provide an overview of the findings, an explanation of existing methodological limitations and areas for future research, and a list of best practices for integrating the technologies we reviewed into the teaching and learning process.

Summary of findings

Findings from our literature review provide preliminary support for including technology as a factor that influences student engagement in existing models (Table 1 ). One overarching theme is that most of the technologies we reviewed had a positive influence on multiple indicators of student engagement, which may lead to a larger return on investment in terms of learning outcomes. For example, digital games influence all three types of student engagement and six of the seven indicators we identified, surpassing the other technologies in this review. There were several key differences in the design and pedagogical use between digital games and other technologies that may explain these findings. First, digital games were designed to provide authentic learning contexts in which students could practice skills and apply learning (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Ke et al., 2016 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ), which is consistent with experiential learning and adult learning theories. Experiential learning theory suggests that learning occurs through interaction with one’s environment (Kolb, 2014 ) while adult learning theory suggests that adult learners want to be actively involved in the learning process and be able apply learning to real life situations and problems (Cercone, 2008 ). Second, students reported that digital games (and gamified elements) are fun, enjoyable, and interesting (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), feelings that are associated with a flow-like state where one is completely immersed in and engaged with the activity (Csikszentmihalyi, 1988 ; Weibel, Wissmath, Habegger, Steiner, & Groner, 2008 ). Third, digital games were closely integrated into the curriculum as required activities (Farley, 2013 ; Grimley et al., 2012 , Ke et al., 2016 ; Liu et al., 2011 ; Marriott et al., 2015 ; Siddique et al., 2013 ) as opposed to wikis, Facebook , and Twitter , which were often voluntary and used to supplement lectures (Dougherty & Andercheck, 2014 Nakamaru, 2012 ; Prestridge, 2014 ; Rambe, 2012 ).

Web-conferencing software and Facebook also yielded the most positive findings, influencing four of the seven indicators of student engagement, compared to other collaborative technologies, such as blogs, wikis, and Twitter . Web-conferencing software was unique due to the sheer number of collaborative features it offers, providing multiple ways for students to actively engage with course content (screen sharing, whiteboards, digital pens) and interact with peers and the instructor (audio, video, text chats, breakout rooms) (Bower, 2011 ; Hudson et al., 2012 ; Martin et al., 2012 ; McBrien et al., 2009 ); this may account for the effects on multiple indicators of student engagement. Positive findings regarding Facebook ’s influence on student engagement could be explained by a strong familiarity and preference for the social networking site (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ; Manca & Ranieri, 2013 ), compared to Twitter which was less familiar or interesting to students (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). Wikis had the lowest influence on student engagement, with mixed findings regarding behavioral engagement, limited, but conclusive findings, regarding one indicator of cognitive engagement (deep processing of information), and no studies pertaining to other indicators of cognitive engagement (motivation, persistence) or emotional engagement.

Another theme that arose was the prevalence of mixed findings across multiple technologies regarding behavioral engagement. Overall, the vast majority of studies addressed behavioral engagement, and we expected that technologies designed specifically for social interaction, such as web-conferencing, wikis, and social networking sites, would yield more conclusive findings. However, one possible reason for the mixed findings may be that the technologies were voluntary in many studies, resulting in lower than desired participation rates and missed opportunities for interaction (Armstrong & Thornton, 2012 ; Fagioli et al., 2015 ; Nakamaru, 2012 ; Rambe, 2012 ; Ross et al., 2015 ; Williams & Whiting, 2016 ), and mandatory in a few studies, yielding higher levels of participation and interaction (Bahati, 2015 ; Gagnon, 2015 ; Roussinos & Jimoyiannis, 2013 ). Another possible reason for the mixed findings is that measures of variables differed across studies. For example, in some studies participation meant that a student signed up for a Twitter account (Tiernan, 2014 ), used the Twitter account for class (Williams & Whiting, 2016 ), or viewed the course-specific Twitter page (Hennessy et al., 2016 ). The pedagogical uses of the technologies also varied considerably across studies, making it difficult to make comparisons. For example, Facebook was used in studies to share learning materials (Clements, 2015 ; Dyson et al., 2015 ), answer student questions about academic content or administrative issues (Rambe, 2012 ), prepare for upcoming exams and share study tips (Bowman & Akcaoglu, 2014 ; DiVall & Kirwin, 2012 ), complete group work (Hou et al., 2015 ; Staines & Lauchs, 2013 ), and discuss course content (Camus et al., 2016 ; Kent, 2013 ; Hurt et al., 2012 ). Finally, cognitive indicators (motivation and persistence) drew the fewest amount of studies, which suggests that research is needed to determine whether technologies affect these indicators.

Methodological limitations

While there appears to be preliminary support for the use of many of the technologies to promote student engagement, there are significant methodological limitations in the literature and, as a result, findings should be interpreted with caution. First, many studies used small sample sizes and were limited to one course, one degree level, and one university. Therefore, generalizability is limited. Second, very few studies used experimental or quasi-experimental designs; therefore, very little evidence exists to substantiate a cause and effect relationship between technologies and student engagement indicators. In addition, in many studies that did use experimental or quasi-experimental designs, participants were not randomized; rather, participants who volunteered to use a specific technology were compared to those who chose not to use the technology. As a result, there is a possibility that fundamental differences between users and non-users could have affected the engagement results. Furthermore, many of the studies did not isolate specific technological features (e.g, using only the breakout rooms for group work in web-conferencing software, rather than using the chat feature, screen sharing, and breakout rooms for group work). Using multiple features at once could have conflated student engagement results. Third, many studies relied on one source to measure technological and engagement variables (single source bias), such as self-report data (i.e., reported usage of technology and perceptions of student engagement), which may have affected the validity of the results. Fourth, many studies were conducted during a very brief timeframe, such as one academic term. As a result, positive student engagement findings may be attributed to a “novelty effect” (Dichev & Dicheva, 2017 ) associated with using a new technology. Finally, many studies lack adequate details about learning activities, raising questions about whether poor instructional design may have adversely affected results. For example, an instructor may intend to elicit higher-order thinking from students, but if learning activity instructions are written using low-level verbs, such as identify, describe, and summarize, students will be less likely to engage in higher-order thinking.

Areas for future research

The findings of our literature review suggest that the influence of technology on student engagement is still a developing area of knowledge that requires additional research to build on promising, but limited, evidence, clarify mixed findings, and address several gaps in the literature. As such, our recommendations for future areas of research are as follows:

Examine the effect of collaborative technologies (i.e., web-conferencing, blogs, wikis, social networking sites ) on emotional and cognitive student engagement. There are significant gaps in the literature regarding whether these technologies affect attitudes, interests, and values about learning; a sense of belonging within a learning community; motivation to learn; and persistence to overcome academic challenges and meet or exceed requirements.

Clarify mixed findings, particularly regarding how web-conferencing software, wikis, and Facebook and Twitter affect participation in learning activities. Researchers should make considerable efforts to gain consensus or increase consistency on how participation is measured (e.g., visited Facebook group or contributed one post a week) in order to make meaningful comparisons and draw conclusions about the efficacy of various technologies for promoting behavioral engagement. In addition, further research is needed to clarify findings regarding how wikis and Twitter influence interaction and how blogs and Facebook influence deep processing of information. Future research studies should include justifications for the pedagogical use of specific technologies and detailed instructions for learning activities to minimize adverse findings from poor instructional design and to encourage replication.

Conduct longitudinal studies over several academic terms and across multiple academic disciplines, degree levels, and institutions to determine long-term effects of specific technologies on student engagement and to increase generalizability of findings. Also, future studies should take individual factors into account, such as gender, age, and prior experience with the technology. Studies suggest that a lack of prior experience or familiarity with Twitter was a barrier to Twitter use in educational settings (Bista, 2015 , Mysko & Delgaty, 2015 , Tiernan, 2014 ); therefore, future studies should take prior experience into account.

Compare student engagement outcomes between and among different technologies and non-technologies. For example, studies suggest that students prefer Facebook over Twitter (Bista, 2015 ; Osgerby & Rush, 2015 ), but there were no studies that compared these technologies for promoting student engagement. Also, studies are needed to isolate and compare different features within the same technology to determine which might be most effective for increasing engagement. Finally, studies on digital games (Beckem & Watkins, 2012 ; Grimley et al., 2012 ; Ke et al., 2016 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ) and face-to-face games (Antunes et al., 2012 ; Auman, 2011 ; Coffey et al., 2011 ; Crocco et al., 2016 ; Poole et al., 2014 ; Scarlet & Ampolos, 2013 ) show similar, positive effects on student engagement, therefore, additional research is needed to determine the degree to which the delivery method (i.e.., digital versus face-to-face) accounts for positive gains in student engagement.

Determine whether other technologies not included in this review influence student engagement. Facebook and Twitter regularly appear in the literature regarding social networking, but it is unclear how other popular social networking sites, such as LinkedIn, Instagram, and Flickr, influence student engagement. Future research should focus on the efficacy of these and other popular social networking sites for promoting student engagement. In addition, there were very few studies about whether informational technologies, which involve the one-way transmission of information to students, affect different types of student engagement. Future research should examine whether informational technologies, such as video lectures, podcasts, and pre-recorded narrated Power Point presentations or screen casts, affect student engagement. Finally, studies should examine the influence of mobile software and technologies, such as educational apps or smartphones, on student engagement.

Achieve greater consensus on the meaning of student engagement and its distinction from similar concepts in the literature, such as social and cognitive presence (Garrison & Arbaugh, 2007 )

Recommendations for practice

Despite the existing gaps and mixed findings in the literature, we were able to compile a list of recommendations for when and how to use technology to increase the likelihood of promoting student engagement. What follows is not an exhaustive list; rather, it is a synthesis of both research findings and lessons learned from the studies we reviewed. There may be other recommendations to add to this list; however, our intent is to provide some useful information to help address barriers to technology integration among faculty who feel uncertain or unprepared to use technology (Ashrafzadeh & Sayadian, 2015 ; Hauptman, 2015 ; Kidd et al., 2016 ; Reid, 2014 ) and to add to the body of practical knowledge in instructional design and delivery. Our recommendations for practice are as follows:

Consider context before selecting technologies. Contextual factors such as existing technological infrastructure and requirements, program and course characteristics, and the intended audience will help determine which technologies, if any, are most appropriate (Bullen & Morgan, 2011 ; Bullen, Morgan, & Qayyum, 2011 ). For example, requiring students to use a blog that is not well integrated with the existing LMS may prove too frustrating for both the instructor and students. Similarly, integrating Facebook- and Twitter- based learning activities throughout a marketing program may be more appropriate, given the subject matter, compared to doing so in an engineering or accounting program where social media is less integral to the profession. Finally, do not assume that students appreciate or are familiar with all technologies. For example, students who did not already have Facebook or Twitter accounts were less likely to use either for learning purposes and perceived setting up an account to be an increase in workload (Bista, 2015 , Clements, 2015 ; DiVall & Kirwin, 2012 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 , Tiernan, 2014 ). Therefore, prior to using any technology, instructors may want to determine how many students already have accounts and/or are familiar with the technology.

Carefully select technologies based on their strengths and limitations and the intended learning outcome. For example, Twitter is limited to 140 characters, making it a viable tool for learning activities that require brevity. In one study, an instructor used Twitter for short pop quizzes during lectures, where the first few students to tweet the correct answer received additional points (Kim et al., 2015 ), which helped students practice applying knowledge. In addition, studies show that students perceive Twitter and Facebook to be primarily for social interactions (Camus et al., 2016 ; Ross et al., 2015 ), which may make these technologies viable tools for sharing resources, giving brief opinions about news stories pertaining to course content, or having casual conversations with classmates rather than full-fledged scholarly discourse.

Incentivize students to use technology, either by assigning regular grades or giving extra credit. The average participation rates in voluntary web-conferencing, Facebook , and Twitter learning activities in studies we reviewed was 52% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Divall & Kirwin, 2012 ; Dougherty & Andercheck, 2014 ; Fagioli et al., 2015 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Rambe, 2012 ; Ross et al., 2015 ; Staines & Lauchs, 2013 ; Tiernan, 2014 ; Williams & Whiting, 2016 ). While there were far fewer studies on the use of technology for graded or mandatory learning activities, the average participation rate reported in those studies was 97% (Bahati2015; Gagnon, 2015 ), suggesting that grading may be a key factor in ensuring students participate.

Communicate clear guidelines for technology use. Prior to the implementation of technology in a course, students may benefit from an overview the technology, including its navigational features, privacy settings, and security (Andrew et al., 2015 ; Hurt et al., 2012 ; Martin et al., 2012 ) and a set of guidelines for how to use the technology effectively and professionally within an educational setting (Miller et al., 2012 ; Prestridge, 2014 ; Staines & Lauchs, 2013 ; West et al., 2015 ). In addition, giving students examples of exemplary and poor entries and posts may also help to clarify how they are expected to use the technology (Shraim, 2014 ; Roussinos & Jimoyiannis, 2013 ). Also, if instructors expect students to use technology to demonstrate higher-order thinking or to interact with peers, there should be explicit instructions to do so. For example, Prestridge ( 2014 ) found that students used Twitter to ask the instructor questions but very few interacted with peers because they were not explicitly asked to do so. Similarly, Hou et al., 2015 reported low levels of knowledge construction in Facebook , admitting that the wording of the learning activity (e.g., explore and present applications of computer networking) and the lack of probing questions in the instructions may have been to blame.

Use technology to provide authentic and integrated learning experiences. In many studies, instructors used digital games to simulate authentic environments in which students could apply new knowledge and skills, which ultimately lead to a greater understanding of content and evidence of higher-order thinking (Beckem & Watkins, 2012 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ). For example, in one study, students were required to play the role of a stock trader in a simulated trading environment and they reported that the simulation helped them engage in critical reflection, enabling them to identify their mistakes and weaknesses in their trading approaches and strategies (Marriott et al., 2015 ). In addition, integrating technology into regularly-scheduled classroom activities, such as lectures, may help to promote student engagement. For example, in one study, the instructor posed a question in class, asked students to respond aloud or tweet their response, and projected the Twitter page so that everyone could see the tweets in class, which lead to favorable comments about the usefulness of Twitter to promote engagement (Tiernan, 2014 ).

Actively participate in using the technologies assigned to students during the first few weeks of the course to generate interest (Dougherty & Andercheck, 2014 ; West et al., 2015 ) and, preferably, throughout the course to answer questions, encourage dialogue, correct misconceptions, and address inappropriate behavior (Bowman & Akcaoglu, 2014 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Roussinos & Jimoyiannis, 2013 ). Miller et al. ( 2012 ) found that faculty encouragement and prompting was associated with increases in students’ expression of ideas and the degree to which they edited and elaborated on their peers’ work in a course-specific wiki.

Be mindful of privacy, security, and accessibility issues. In many studies, instructors took necessary steps to help ensure privacy and security by creating closed Facebook groups and private Twitter pages, accessible only to students in the course (Bahati, 2015 ; Bista, 2015 ; Bowman & Akcaoglu, 2014 ; Esteves, 2012 ; Rambe, 2012 ; Tiernan, 2014 ; Williams & Whiting, 2016 ) and by offering training to students on how to use privacy and security settings (Hurt et al., 2012 ). Instructors also made efforts to increase accessibility of web-conferencing software by including a phone number for students unable to access audio or video through their computer and by recording and archiving sessions for students unable to attend due to pre-existing conflicts (Andrew et al., 2015 ; Martin et al., 2012 ). In the future, instructors should also keep in mind that some technologies, like Facebook and Twitter , are not accessible to students living in China; therefore, alternative arrangements may need to be made.

In 1985, Steve Jobs predicted that computers and software would revolutionize the way we learn. Over 30 years later, his prediction has yet to be fully confirmed in the student engagement literature; however, our findings offer preliminary evidence that the potential is there. Of the technologies we reviewed, digital games, web-conferencing software, and Facebook had the most far-reaching effects across multiple types and indicators of student engagement, suggesting that technology should be considered a factor that influences student engagement in existing models. Findings regarding blogs, wikis, and Twitter, however, are less convincing, given a lack of studies in relation to engagement indicators or mixed findings. Significant methodological limitations may account for the wide range of findings in the literature. For example, small sample sizes, inconsistent measurement of variables, lack of comparison groups, and missing details about specific, pedagogical uses of technologies threaten the validity and reliability of findings. Therefore, more rigorous and robust research is needed to confirm and build upon limited but positive findings, clarify mixed findings, and address gaps particularly regarding how different technologies influence emotional and cognitive indicators of engagement.

Abbreviations

Learning management system

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Schindler, L.A., Burkholder, G.J., Morad, O.A. et al. Computer-based technology and student engagement: a critical review of the literature. Int J Educ Technol High Educ 14 , 25 (2017). https://doi.org/10.1186/s41239-017-0063-0

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PeerJ Comput Sci

Today’s computing challenges: opportunities for computer hardware design

Woorham bae.

1 Circuits Department, Ayar Labs, Santa Clara, CA, USA

2 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA

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The following information was supplied regarding data availability:

This work does not include any code as it is a literature review.

Due to the explosive increase of digital data creation, demand on advancement of computing capability is ever increasing. However, the legacy approaches that we have used for continuous improvement of three elements of computer (process, memory, and interconnect) have started facing their limits, and therefore are not as effective as they used to be and are also expected to reach the end in the near future. Evidently, it is a large challenge for computer hardware industry. However, at the same time it also provides great opportunities for the hardware design industry to develop novel technologies and to take leadership away from incumbents. This paper reviews the technical challenges that today’s computing systems are facing and introduces potential directions for continuous advancement of computing capability, and discusses where computer hardware designers find good opportunities to contribute.

Introduction

These days, the world has been evolving very fast in various areas. The focus of technology development has been also switching to realize better human experience, convenience, and happiness, rather than old focuses such as mass production, automation, or cost reduction. Such rapid changes severely impact on the silicon industry, which has been responsible for the computing capability of the planet for several decades. The impact can be either positive or negative; it can provide more opportunities but also introduce many challenges at the same time. In fact, the opportunities are all about data ( Horowitz, 2014 ; Kim, 2015 ). That is mainly because the world needs more electronics to handle the data. As mentioned above, the world is pushing to realize a whole bunch of things (such as smart cities, security, autonomous vehicles…) for better human experience, convenience, and happiness. In order to do that, we need to create, replicate, and process all the data. Accordingly, all the surveys predict that the amount of digital data will increase exponentially in the next 10 years. For example, Fig. 1 shows Cisco’s two reports on the amount of data creation in the world, which were released in 2017 and 2019 ( Cisco Visual Networking Index, 2017 ; Cisco Visual Networking Index, 2019 ). Both reports predict that the amount of data will grow exponentially, but the 2019 report tells that the data has been created more than that expected 2 years before, and it will increase more rapidly.

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The world is driven by data, and electronics are responsible for handling those data, which means that we need to create more and more electronics devices. Cisco predicts that the number of electronic devices will increase by almost twice in 5 years. Nvidia gives a bit more aggressive prediction, such that the number of total connected devices will increase by 16 times in 7 years ( Shao, 2019 ). No matter how much it is, everybody expects that there will be more needs for electronic devices.

On the other hand, the challenges are also all about data. Here are some critical concerns one can raise in the age of such exploding data. How do we process such amount of data? Where should we store the data? How do we communicate with the data? And, what happens if we keep the same energy efficiency while the amount of data is exploding? Going back to the Fig. 1 , where the Cisco’s projection is shown, the amount of digital data is going to explode. If so, what happens if we keep the same energy efficiency for processing, storing, and communicating? Then the energy consumption will increase at the same rate as the data explosion, which is definitely not affordable. It is known that we are already consuming the largest portion of energy in the Earth for handling the data with electronics ( Bae, Jeong & Jeong, 2016 ; Whitney & Delforge, 2014 ; Pierce, 2018 ); definitely such amount of data should not be affordable. From this observation, we would say that the energy efficiency must be improved proportional to the data explosion, at least to keep the same amount of energy consumption.

In fact, such explosion of data is not something that started yesterday, even though there might be some difference in degree. Hence, it is worthwhile trying to learn something from the history, how we did handle such exploding data before. Figure 2 shows a simplified computing system, where we can see a logic (processor) IC and a memory IC, and an interconnect link between them. Basically, in order to handle more data, we need higher processing speed, interconnect bandwidth, and memory density. Figure 2 also shows a simple summary of how we managed to enable it. For the processor side, the CMOS (complementary metal-oxide silicon) technology scaling, which is generally represented by Moore’s law ( Moore, 1965 , 1975 ), enables a transistor to be faster and consume even less power ( Holt, 2016 ; Bohr & Young, 2017 ; Mak & Martins, 2010 ; Yeric, 2019 ). Once we have a faster transistor, we can raise the clock rate for faster processing. Once after the power scaling of transistor has been retarded due to some physical reasons (i.e., leakage current), people introduced parallelism such as multi-core processing to increase the processing speed without increasing the clock rate ( Danowitz et al., 2012 ). For the memory side, the scaling of device footprint enabled a higher memory density ( Hwang, 2002 ). However, extensive scaling led to many challenges, which were overcome by the memory industry with process innovations such as higher aspect ratio of DRAM, and material innovations like high-k materials ( Mueller et al., 2005 ; Sung et al., 2015 ; Jang et al., 2019 ). For the interconnect side, the transistor scaling has been also a key enabler for a higher bandwidth, because a faster transistor makes a circuit faster ( Daly, Fujino & Smith, 2018 ; Horowitz, Yang & Sidiropoulos, 1998 ). However, the electrical channels (wires) which bridge separate ICs cannot be scaled with the silicon technology, as they present in the physical world, not in the digital IC world. That is, an electrical channel has a finite bandwidth so that high-frequency components of transmitted signal attenuate over the channel. As a result, interconnect engineers had to make many innovations in equalization circuits which compensate the channel loss at high frequency, that is to equalize the channel response at low and high frequency ( Horowitz, Yang & Sidiropoulos, 1998 ; Dally & Poulton, 1997 ; Zerbe et al., 2003 ; Stojanovic et al., 2005 ; Choi, Hwang & Jeong, 2004 ). They also introduced time-interleaving technique, which is something like the parallelism, to achieve very high speed even above the transistor limit ( Kim & Horowitz, 2002 ; Lee, Dally & Chiang, 2000 ; Musah et al., 2014 ; Bae et al., 2017 ).

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However, these legacy approaches cannot be good solutions for these days and the future. First of all, we are about to lose the almighty scaling. The scaling has not been fully finished yet; however, it has been a while since the power scaling started being retarded as discussed earlier. As a result, increasing the clock frequency is no longer available because we do not want to burn the chip out. The parallelism was introduced to overcome such challenge, but it has also hit the limit because of the same heat dissipation issue. Only a fraction of the multi-cores can be turned on at the same time, which is called “dark silicon” ( Yeric, 2019 ; Esmaeilzadeh et al., 2011 ).

The similar issue happened to the memory, that is, the scaling has been retarded which limits the increase in the memory density. The scaling also introduced many non-idealities so that there are many higher-level assistances which burden memory module and increase the latency of the memory. For the interconnect side, the channel loss becomes very significant as the required interconnect bandwidth increases, so the equalization circuitry consumes too much power. It will be tougher as the scaling is ending because we can no longer take advantage of faster transistors. To summarize this section, the legacy solutions for handling data explosion will not be as effective as they used to be for the today’s and future computer. From the following section, we will discuss on the possible solutions that enable the continuous advance in computing capability for the next 10 years.

The remainder of this article is organized as follows. “Logic (System Semiconductor)” presents potential solutions for addressing the challenges of the logic and opportunities for the computer hardware design industry and engineers. “Memory and Storage” describes the recent innovations from the memory industry and discusses the future direction. In addition, the opportunities for design engineers from the revolution of the memory devices will be discussed. “Interconnect” discusses the recent trend of the interconnect technology and potential solutions to resolve the challenges that the interconnect technology is facing. Finally, conclusions are provided in “Conclusions”.

Survey methodology

This review was conducted in Sept.–Oct. 2020. Three different approaches were used to collect research articles:

Searching Google scholar and IEEE Xplore with various keywords such as Moore’s law, CMOS scaling, high-bandwidth memory, V-NAND, crosspoint memory, transceiver, PAM-4, and silicon photonics.
Starting from an initial pool of articles and then move back and forth between their citations and references.
Selecting articles based on their impact and credibility; Prioritizing articles with high citations or from top conferences and journals of the fields, such as JSSC, TCAS, TCAD, TED, AELM, ISSCC, VLSI, IEDM.

Logic (system semiconductor)

Efficient computing with specialized ic.

In this section, the technology directions for silicon logic to maximize the opportunity for the hardware design is discussed. While dealing with technology development of computation logic, it is inevitable to discuss the scaling limit of semiconductor process technology, the end of Moore’s law. In fact, since 2014, there have been at least one of plenary talks at International Solid-State Circuits Conference (ISSCC) that discuss on preparing the end of the Moore’s law, for example Kim (2015) and Vandersypen & van Leeuwenhoek (2017) . So, let us take a quick look at where the scaling limit comes from. In his talk at ISSCC2015 ( Kim, 2015 ), the president of Samsung Electronics told that the physical limit of transistor dimension is around 1.5 nm, which is given from Heisenberg’s uncertainty principle. However, he also told that he expected that the practical limit will be 3 nm. After 5 years, now, the 7 nm technology is already widely available in the industry. And the leading foundries such as TSMC and Samsung Electronics are already working on 5 nm and 3 nm technology development, which means that we are almost there.

As a result, recalling the energy discussion in the “Introduction”, the appropriate question for this point should be how we can improve the energy efficiency without scaling. We can find some hints from today’s mining industry, the cryptocurrency mining, where the computing energy efficiency is directly translated to the money. Recalling 2017, when the cryptocurrency value hit the first peak, the readers may remember that the graphics processing unit (GPU) price became very expensive. It is simply because the GPU is much more efficient than central processing units (CPU), so mining with GPU gave more profit margin. Then, why the GPU is much efficient compared to CPU?

It is because it is specialized. CPU is more generic, but the GPU is more specific. That is, there is a computing trade-off between the flexibility and the efficiency. After finding that, people went to field-programmable gate array (FPGA) for cryptocurrency mining for better efficiency, and eventually they end up with designing application-specific integrated circuit (ASIC) just for the mining. Figure 3 shows the survey of various cryptocurrency miners, where we can find an ASIC miner provides 10 4 times better efficiency than a CPU miner. From the observation, we can conclude that such a huge gain comes from the design of specialized ICs. To summarize, making specialized ICs is one of the top promising solution for the efficient computing. In accordance with that, the foundry companies would diversify their process technology instead of scaling it down, for example the Global Foundries 45 nm CLO process, which is specialized to silicon photonics ( Rakowski et al., 2020 ).

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Productivity problem of specialization

We found that the specialization would be a potential solution to resolve the energy problem and to retain the continuous advance of computing. However, there are also some downsides of the specialization, so we need to investigate how profit is made in the new age with the specialization. In a simplified model in Fig. 4A , a fabless company shipped 1 million units of a generic chip before, but they are planning to design 10 specialized chips in 10 different processes to meet the better efficiency requirement. At the same time, they are expecting they can ship 2 million chips in total as there will be more demand of electronics. In the model, the company is currently making $3 million profit. On the right side of Fig. 4A , a linear extrapolation is made to when the company designs 10 specialized chips and total shipping is doubled. Note that all the cost is extrapolated in linearly proportional to the amount of production.

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Case study of (A) ideal case, (B) practical case, (C) practical case with reduced design time.

However, it is too optimistic projection. Figure 4B shows a bit more realistic model. The revenue and production cost are indeed proportional to the amount of shipping. However, does it make sense to extrapolate other expenses? Of course, the answer is no. For example, the amount of manpower cannot be scaled linearly. To design a single complete chip, they need analog engineers, digital engineers, manufacturing engineers and more. Therefore, it makes no sense that only 4 engineers can make a chip which used to be made by 20 engineers, even though there must be some amount of efforts that can be shared among the chip designs. So, the model in Fig. 4B assumes 10 engineers can design a specialized chip A0. If so, the profit becomes minus. The calculation here is very rough, but at least we can observe a large fraction of design cost is not scaled with the amount of production. The company would raise the price, but customers will not be happy with that. Then, is the specialization a false dream?

The most reasonable solution here is to reduce the design time, since such design costs are proportional to the design time, as shown in Fig. 4C . For example, if they can reduce the design time by half, they can reduce the expenses by half, then they can make more profit. As mentioned earlier, they are designing 10 different but similar chips, and there is some amount of sharable efforts. That means, if they maximize the amount, they should be able to reduce the design time considerably.

Reducing design time by reusing design

Then what should we try to maximize shareability? Generally speaking, we can say the analog and mixed signal (AMS) circuit design is usually the bottleneck of reducing design time. That is mainly because AMS circuit designs highly rely on human’s heuristic knowledge and skills, compared to the digital design. Moreover, the design complexity has been increased as technology scales down, due to the complex design rules and digital-friendly scaling of CMOS technology, which is represented by the number of design rules shown in Fig. 5A , where we can find the design complexity has been increased exponentially as the technology scales down ( White ; Whitcombe & Nikolic, 2019 ; Han et al., 2021 ). Figure 5B shows a general design flow of an AMS circuit. Once we decide a circuit topology, we carefully size the transistor dimensions based on some calculations, and run simulation using CAD tools. If the simulation result is not positive, we go back and tweak the sizing. Once we meet the spec with the schematic simulation, we proceed to draw the layout mask, after that we run parasitic extracted (PEX) simulation and check the result again. Based on the result, we have to go back and forward many times until the performance of the circuit is fully optimized. The main issue here is that most of time is spent for drawing layout, and its complexity has been increasing as shown in Fig. 5A . One may ask why we do not try to automate the analog design as we did for the digital design. However, in fact, it is hard to say we can do it for the layout design in the near future because there are only a few ways to do it right, however there are billions of ways to do it wrong. That means, to make the automation tool work correctly, a designer should constrain the tools very precisely ( Habal & Graeb, 2011 ; Lin, Chang & Lin, 2009 ), so they spend most of the design time constraining the tools, which is not very efficient ( Chang et al., 2018 ). That is the main reason why the engineers in this field rarely use such automation tools.

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In fact, a better way is reusing, because reuse is a bit easier than automation. For example, we can just grab a good designer who knows how to do it correctly and let him/her do the design. At the same time, we enforce him/her to write down every single step he would do to create a correct output into an executable script (often called as a generator). Then the script has the way of doing right of the good designer, so the output should be correct no matter who run the script. However, because transistor shapes are different between process technologies, it is hard to automatically capture a design-rule-compatible shape only with the script, without intervention of designer’s heuristic knowledge. Therefore, such script-based approach works well in a single process technology, however it would face many challenges when ported to another technology. To address such portability issue, template-based approaches have been proposed in many works ( Crossley et al., 2013 ; Yilmaz & Dundar, 2008 ; Castro-Lopez et al., 2008 ; Martins, Lourenco & Horta, 2013 ; Kunal et al., 2019 ; Wang et al., 2019 ). Instead of letting a layout script draw a layout from scratch, designers prepare design-rule-aware templates of primitive components. The script assembles the templates by following the way an expert designer pre-defined. It is like a Lego block, when we buy a Lego package, there are many unit blocks (templates) and an assembly manual (script), as shown in Fig. 6 .

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Such reuse-based approach is very attractive for the future of specialization, however there are some hurdles that the designers must overcome. In fact, the hurdle is not a matter of development of elegant CAD tools. Here is an example based on the author’s engineering experiences. The author has used three different frameworks for helping such reusing process, the Laygo, XBase, and ACG ( Berkeley Analog Generator, 2021a , 2021b ; Ayar Custom Generator, 2021 ). They are quite different each other as summarized in Fig. 7 , for example the Laygo defines the templates more strictly so it more limits the degree of freedom, whereas the ACG has loose template definitions. There are pros and cons; the Laygo reduces the number of ways to do in wrong way for easier portability at the cost of sacrificing the degree of freedom. The ACG allows freedom however it burdens a designer spend more time on writing a portable script. That is, to summarize, there is just a trade-off. Designers should spend more time to make it portable (left side of Fig. 7 ) or they should spend more time to make it as good as custom design (right side of Fig. 7 ). For either way, a good script has to have flexible parameterizations ( Chang et al., 2018 ). So, it is not a matter of which tool we would use. Instead, what is more important is whether a designer is willing to use this methodology or not, because analog designers are not generally familiar with such parameterization. In addition, writing a design script requires more skillsets and insights compared to custom designs. As a result, to take full benefit of the reusing, the designers must be patient and be willing to learn something, which is the main hurdle.

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Once we overcome the hurdle, there will be more opportunities to further improve the productivity. For example, it allows a machine to accomplish the entire design iteration shown in Fig. 5B ( Settaluri et al., 2020 ). Conventionally, it was believed that the design space is too huge to fully automate the optimization, even with schematic-only simulation. However, recent progress in deep learning technology enable handling such huge space, so a machine can handle the schematic optimization ( Hakhamaneshi et al., 2019 ). However, as mentioned earlier, the layout automation is almost impossible so the machine must struggle with the layout loop. The script-based layout reuse can bridge the gap: (1) The machine sizes the schematic parameters. (2) The layout script generates a layout from the parameters. (3) The machine runs PEX simulations and checks the results. (4) Based on the results, the machine resizes the parameters and repeats (1)–(3) until the circuit is fully optimized. Many efforts should be preceded to fully realize such AI-based design, but it is evident that there will be tons of opportunities along the way.

Memory and storage

Memory scaling limit and 3-d integration.

In the previous section, we discussed that the specialization and reuse of the design process will be one of the solutions for the challenges that the logic side is facing. In this section, recent progress and future technology for memory will be presented, and then the opportunities for hardware designers to contribute to the technology innovation will be discussed. In fact, in the memory industry, physics and device engineering have played more critical role compared to design engineering. For example, circuit topology of bit-line sense amplifier in memory module has not been changed for decades while the memory devices have been evolving. This trend is likely to continue in the future, however it is expected that the memory industry will need more innovations from design.

Let us briefly review the challenges that current memory is facing, which is mainly because of the scaling limit as discussed in the “Introduction”. Basically, higher memory density is the top priority which has been enabled by the process scaling. For DRAM, however, lower capacitance due to extensive scaling results in many challenges such as short data retention, poor sensing margin, and interference. As a result, the scaling is no longer as effective as it used to be. Similarly, NAND flash also experiences many non-idealities introduced by the extensive scaling, such as short channel effect, leakage, and interference. Again, the scaling is not useful as it used to be. Recently, however, memory industry has found a very good way rather than pushing the device scaling too hard, they found the solutions from 3D stacking. Figure 8 shows the recent innovations with 3D stacking that have been developed for DRAM and NAND flash, high-bandwidth memory (HBM) and vertical NAND (V-NAND) ( Lee et al., 2014 ; O’Connor, 2014 ; Tran, 2016 ; Jun et al., 2017 ; Xu et al., 2015 ; Kim, Lee & Kim, 2016 ; Kim et al., 2009 , 2017 ; Tanaka et al., 2016 ; Im et al., 2015 ; Park et al., 2014 ). In HBM, multiple DRAM dies are stacked, and they are connected by through silicon vias (TSV). A base logic die can be used to buffer between the DRAM stack and the processing unit (host SoC). The logic die and the processing unit are connected through micro-bumps and silicon interposer. Because the memory stack and the processing unit are not integrated in 3D manner, the HBM is often considered as 2.5D integration. Unique features of the HBM such as low capacitance of TSV, 2.5D integration, and high interconnect density of silicon interposer enable high capacity (not always), low power, and high bandwidth compared to legacy DRAM ( O’Connor, 2014 ; Tran, 2016 ; Jun et al., 2017 ; Ko et al., 2020 ). On the other hand, in NAND flash, the memory cells themselves are stacked. Interestingly, nowadays it is higher than 100 layers. In fact, these much of innovations on the capacity, as well as advancements on processing units, burden more on the interconnect side for higher bandwidth and lower latency ( Jun et al., 2017 ; Patterson, 2004 ; Hsieh et al., 2016 ). In other word, it requires more contributions from interconnect design so that it is an opportunity for design engineers. For example, as solid-state drive (SSD) capacity has dramatically increased with the V-NAND, the legacy serial-ATA (SATA) interface is not fast enough to provide enough bandwidth. As a result, recent SSD products use NVM Express (NVMe) protocol which is based on peripheral component interconnect express (PCIe) interface. In fact, the PCIe is one of the standards that is evolving very quickly; the industry was working on 16-Gb/s PCIe gen4 in 2016, but started working on 32-Gb/s gen5 since 2018, and 64-Gb/s gen6 specification is going to be released soon ( Vučinić et al., 2014 ; Ajanovic, 2009 ; Budruk, 2007 ; Cheng et al., 2010 ; Li et al., 2018 ).

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Since multiple dies are stacked in the HBM, there are more interconnects that are required, and there are unique challenges which can be distinguished from a conventional interconnects, which means there are plenty of works that the interconnect design has to do. For example, the stacked DRAM is communicating with processing unit through the silicon interposer channel, which is quite different to the conventional channels such as channel response and crosstalk ( Ko et al., 2020 ; Liu, Ding & Jiang, 2018 ). In addition, the stacked DRAM dies are connected by TSV links whose characteristic is also very different ( Lee et al., 2015 , 2016 ; Kim et al., 2012 ). And there is also a logic die where a HBM PHY is used to bridge the DRAM stack and the host SoC. There are also unique issues, for example thermal stability issue due to the stacking ( Sohn et al., 2016 ; Ko et al., 2019 ), which should be overcome by hardware design.

Introducing new memory devices

In addition to those efforts discuss above, the memory industry is trying to introduce new non-volatile memory (NVM) devices, for example phase-change RAM (PRAM) or resistive RAM (RRAM, also referred to as memristor), whose conceptual diagram is shown in Fig. 9 . These devices have only two ports so that it has a smaller footprint of 4F 2 , and they are able to be integrated in crossbar array and easy to stack ( Wong et al., 2010 , 2012 ; Bae et al., 2017 ; Yoon, Kim & Hwang, 2019 ; Foong & Hady, 2016 ; Kau et al., 2009 ; Yoon et al., 2017 ; Liu et al., 2013 ). In addition, they can be formed in back-end process so that they can be integrated on top of the CMOS peripheral circuits, which makes their effective density even higher and realizes a true sense of 3D integration. Moreover, the devices themselves are much faster than NAND device. Note that a faster device means that we need a faster interconnect not to degrade the memory performance due to the interconnect. That is, there will be more demand on high performance interconnect design, similar to what happens on the HBM and V-NAND cases.

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These devices have many attractive features, however, there are plenty of challenges that need to be overcome to make them succeed in the industry. For example, their operation and side effects are not yet fully modeled; and the PRAM has a reliability issue which is called snapback current during write operation; and the RRAM has a sneak current issue which distorts readout operation as well as write ( Yoon et al., 2016 ); and the variation effect is much larger than the legacy devices because of their intrinsic non-linearity. In fact, these kinds of challenges fall into categories where design engineers can do better than device engineers. For example, they can build a good physics-aware model to bring these devices into an accurate and complex hardware simulation, to enable collaborative optimization between circuits and devices. Because of their non-linearity and hysteresis, some special techniques need to be developed to ensure that they converge in a huge array-level simulation, while capturing the realistic behavior ( Bae & Yoon, 2020 ; Wang, 2017 ; Kvatinsky et al., 2012 ; Linn et al., 2014 ; Chen & Yu, 2015 ). On the other hand, some circuit design techniques can be introduced to mitigate the snapback current ( Kim & Ahn, 2005 ; Redaelli et al., 2004 ; Parkinson, 2011 ). Also, circuit designers can propose variation-tolerant or variation-compensated techniques to address the variation issue ( Athmanathan et al., 2016 ; Park et al., 2017 ; Hwang et al., 2010 ; Bae et al., 2018 ), or sneak-current cancellation scheme for the sneak current issue ( Vontobel et al., 2009 ; Shevgoor et al., 2015 ; Bae et al., 2016 ). In addition, looking further forward, RRAM is regarded to be a promising candidate for in-memory computing or neuromorphic computing, because of its capability to store analog weights ( Alibart, Zamanidoost & Strukov, 2013 ; Prezioso et al., 2015 ; Yoo, 2019 ; Xue et al., 2019 ; Kim & Williams, 2019 ; Yoon, Han & Bae, 2020 ; Wang et al., 2019 ). These approaches are believed to overcome the limitation of the current computer architecture, where we need tons of inter-disciplinary research opportunity to realize them.

To summarize this section, the introduction of the 3D integration and the new memory devices is believed to overcome the scaling limit of memory devices, and it needs a lot of supports from hardware designers and gives many opportunities to contribute.

Interconnect

Trend survey and challenges.

In this section, the challenges and potential solutions of computer communication interconnect are presented. Recalling the “Introduction”, increase in data and advancement in computing require higher speed interconnect, however the electrical channel becomes more and more inefficient as the data rate increases. Figure 10 shows an architectural diagram of a general interconnect, which serializes parallel input to high-speed non-return-to-zero (NRZ) bitstream and transmits it through electrical channel (wire), and then de-serializes the serial input to parallel at the receive side ( Bae, 2020 ; Chang et al., 2003 ; Mooney et al., 2006 ; Bulzacchelli et al., 2006 ). It is notable that this architecture has not been changed over last 15 years. Since then, the advancements mainly focus on improving building blocks of the given golden architecture, such as designing a better equalizer to provide a better compensation for the channel loss.

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Let us have a deeper look at what causes the challenges on the computer interconnect. As mentioned earlier, the electrical channels do not scale with the silicon technology. However, the interconnect partially takes advantage of the technology scaling, because faster transistors enable a better circuit to overcome the increased channel loss. Figure 11A shows a survey from the state-of-the-art published works ( Tamura et al., 2001 ; Haycock & Mooney, 2001 ; Tanaka et al., 2002 ; Lee et al., 2003 , 2004 ; Krishna et al., 2005 ; Landman et al., 2005 ; Casper et al., 2006 ; Palermo, Emami-Neyestanak & Horowitz, 2008 ; Kim et al., 2008 ; Lee, Chen & Wang, 2008 ; Amamiya et al., 2009 ; Chen et al., 2011 ; Takemoto et al., 2012 ; Raghavan et al., 2013 ; Navid et al., 2014 ; Zhang et al., 2015 ; Upadhyaya et al., 2015 ; Norimatsu et al., 2016 ; Gopalakrishnan et al., 2016 ; Shibasaki et al., 2016 ; Peng et al., 2017 ; Han et al., 2017 ; Upadhyaya et al., 2018 ; Wang et al., 2018 ; Depaoli et al., 2018 ; Tang et al., 2018 ; LaCroix et al., 2019 ; Pisati et al., 2019 ; Ali et al., 2019 , 2020 ; Im et al., 2020 ; Yoo et al., 2020 ), where we can confirm the correlation between the technology node and the data rate. On the other hand, however, overcoming the increased channel loss has become more and more expensive as the loss is going worse as the bandwidth increases; the equalization circuits consume too much power to compensate the loss, which makes people hesitant to increase the bandwidth. As a result, the tendency has been weakened after 32-nm node. Figure 11B shows the bandwidth trend over years, which evidently shows the bandwidth increase has saturated at around 28–40 Gb/s for years.

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Recently, a dramatic change has been made to break the ice. An amplitude modulation technique, which is called 4-level pulse-amplitude modulation (PAM-4), has been adopted in the industry ( Upadhyaya et al., 2018 ; Wang et al., 2018 ; Depaoli et al., 2018 ; Tang et al., 2018 ; LaCroix et al., 2019 ; Pisati et al., 2019 ; Ali et al., 2019 , 2020 ; Im et al., 2020 ; Yoo et al., 2020 ). With PAM-4, the interconnect can transmit two bits in one-bit period, which doubles the effective bandwidth over NRZ. This dramatic change enables the interconnect bandwidth higher than 50 Gb/s as observed in Fig. 11 , and most of latest specifications whose speed are higher than 50 Gb/s employ the PAM-4. In addition, all the golden architecture except for very front-end circuits do not have to be changed with PAM-4, which makes it more attractive.

However, we have to ask if this approach is sustainable or not. We doubled the data rate by adopting PAM-4, then can we do the same with PAM-8 or PAM-16? Fig. 12 shows the comparison between those modulations. The basic concept of PAM-4 is to transmit two bits at the same time, so it achieves 2x higher data rate at the same Nyquist frequency. However, there are 4 signal levels (3 stacked eyes) instead of 2 levels (1 eye), the signal-to-noise ratio (SNR) degrades by 3x, or 9.5 dB. It also introduces some other non-idealities such as non-linearity and CDR complexity, so it can be worse. These days, PAM-4 is justified because the benefit from the higher bandwidth exceeds the SNR loss. We can do the same calculation for PAM-8. It transmits 3 bits while PAM-4 transmits 2 bits, so we get 1.5x higher bandwidth, whereas there are 7 eyes over PAM-4’s 3 eyes, which is equivalent to 7.4-dB SNR degradation. That is, the benefit of PAM-8 is lower than what we can get from PAM-4. The same calculation for PAM-16 is also given in the Fig. 12 , where we can find the benefit gets even smaller than PAM-8. From the observation, we can conclude that the amplitude modulation will not be a sustainable solution while the channel capacity and the noise keep the same ( Shannon, 1948 ).

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Future directions

As an alternative, we would rather start modifying the golden architecture. One of the potential candidates is a forwarded-clock architecture, which has been explored in several literatures ( Casper et al., 2006 ; Li et al., 2014 ; Ragab et al., 2011 ; Casper & O’Mahony, 2009 ; Hossain & Carusone, 2011 ; Chung & Kim, 2012 ; Bae et al., 2016 ). The bit-error-rate (BER) of an interconnect is a function of the amplitude noise (SNR) and the timing noise (jitter) ( Bae et al., 2016 ). If the SNR becomes worse as the channel loss increases or PAM is used, we can try cancel it out by improving the timing noise. However, in the conventional architecture, way of reducing the timing noise is very limited other than burning more power. Instead, we can forward the transmitter clock to the receiver along with data. Because the timing noise of the forwarded clock and the data are correlated, sampling the data with the forwarded clock cancels the correlated component out hence the effective timing noise at the receive side is minimized. With that, the signaling power and the CDR complexity can be significantly reduced at the same BER, at the cost of just one extra clock channel.

On the other hand, we can also make a bigger change on the architecture. In an analog-to-digital converter (ADC)-based interconnect or digital signal processing (DSP)-based interconnect ( LaCroix et al., 2019 ; Pisati et al., 2019 ; Ali et al., 2019 ; 2020 ; Im et al., 2020 ; Yoo et al., 2020 ; Harwood et al., 2007 ; Chen & Yang, 2010 ; Wang et al., 2018 ; Palermo et al., 2018 ), the analog front-end circuits of the receiver are replaced by a high-speed ADC, and a large fraction of the equalization and CDR stuffs are done in the digital domain. With that, an extensive equalization with dense digital logic is enabled. In addition, PAM-4 justifies the use of ADC because it already requires simple ADC-like front-end as it transmits and receives multiple data levels. The DSP-based interconnect is maturing rapidly these days, however there are still lots of works to come, for example design techniques for building high-speed ADC or resolving high latency of DSP-based receiver.

For a long-term solution, more dramatic change would be required, because the fundamental limit comes from the limited bandwidth of electrical channels. As a result, replacing the electrical channel with optical channel whose bandwidth is almost infinite is believed to be a very promising and eventual solution ( Miller, 2000 ; Young et al., 2009 ; Jeong, Bae & Jeong, 2017 ; Thraskias et al., 2018 ). Conventionally, the optical interconnect has been used for long-distance telecommunication whereas the electrical interconnect is responsible for short-distance computer communication. It is mainly because the optical interconnect consumes much higher power because of power-hungry optical devices and electrical-optical interfaces. On the other hand, because of its lossless nature, the communication distance has little impact on the optical communication performance. However, the electrical interconnect exhibits lower power consumption at short-reach communication, however its power consumption dramatically increases as the communication distance increases because the electrical channel loss increases exponentially with the distance. As a result, there is a critical length where the optical interconnect becomes more efficient than the electrical interconnect, as shown in Fig. 13A ( Cho, Kapur & Saraswat, 2004 ). In similar manner, when the required data rate increases, the power consumption of the electrical interconnects increases exponentially even at the same distance, however it has little impact on the optical interconnect as shown in Fig. 13B . Therefore, the critical length is expected to become shorter as the data rate keep increasing, which make us believe the optical interconnect will be eventually used for computer communication ( Cho, Kapur & Saraswat, 2004 ). However, to realize it, the energy efficiency of optical interconnects must be improved a lot. Currently, the bandwidth-efficiency product of commercial optical interconnects (long-reach) is almost 1,000x lower than that of electrical interconnects ( Sun et al., 2020 ). Then why does a present optical interconnect consume that much power? There can be many reasons, but one of the main reasons is that it is not monolithically integrated. When we look into an optical communication module, there are multiple ICs such as photonics transmitter, receiver, electronic driver IC, retimer IC, and microcontroller. As a result, there are so many interfaces even in a single communication module, where electrical signals come out to the real analog world and experience bulky parasitics, which leads to such poor energy efficiency. That is, monolithic integration where optical devices and VLSI circuits are integrated in a single chip can be a solution for reducing the power consumption ( Sun et al., 2015a , 2015b , 2020 ; Narasimha et al., 2007 ). In addition to the monolithic integration, dense wavelength division multiplexing (DWDM) enables transmitting multiple data streams through a single optical fiber, which significantly improves the bandwidth density of optical interconnect. DWDM can be regarded as another modulation, but it does not degrade SNR as much as PAM. In fact, it has been more than 30 years ago that the optical interconnect began to gain attention, but there have been no succeed until recently. However, recently, the accumulated efforts are coming out with promising engineering samples such as 5-pJ/bit monolithic DWDM ( Sun et al., 2020 ), 6-pJ/bit 112-Gb/s PAM-4 ( Li et al., 2020 ), so the time will really come soon.

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Conclusions

In this paper, the challenges that the current computing system (logic, memory, interconnect) is facing are reviewed. For the logic, the cryptocurrency miners are surveyed which leads to the future direction of specialization, but the downside of specialization is also discussed with an example of a fabless company. For the memory, the challenges and opportunities for design engineering in conjunction with device engineering are reviewed, whereas other reviews tend to focus on devices. For the interconnect, the state-of-the-art works are surveyed, and the recent trends and challenges are discussed. From the reviews and surveys for each part, the solutions and opportunities for those challenges are discussed, which are summarized in Fig. S2 . For the logic side, the specialization is proposed for achieving higher efficiency after Moore’s law, and the reusing is also proposed for addressing the productivity issue of the specialization. On the memory side, 3D integration of memory dies or cells and introduction of new NVM devices are expected to overcome the memory density issue. At the same time, they request substantial assistances from design engineers, for example high-performance interconnects, robust physics-aware device modeling, and tons of design techniques to overcome the device limits. Finally, the interconnect side needs to innovate its conventional architecture which has not been changed for a while, and eventually it must drive the optical interconnect.

Supplemental Information

Supplemental information 1, supplemental information 2, funding statement.

The authors received no funding for this work.

Additional Information and Declarations

Woorham Bae is employed by Ayar Labs. Woorham Bae is also an Academic Editor for PeerJ.

Woorham Bae conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

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CHALLENGES IN COMPUTER SYSTEMS SERVICING NCII EXPERIENCED BY THE GRADE 12 STUDENTS IN THE SISTERS OF MARY SCHOOL-GIRLSTOWN

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(PDF) Effects of Computer-Based Training in Computer Hardware Servicing
Effects of Computer-Based Training in Computer Hardware Servicing on Students' Academic Performance January 2022 International Journal of Technology-Enabled Student Support Services 12(1):1-13
Computer Hardware Servicing and Maintenance Trainer
The purpose of this study is to enhance the troubleshooting, system configuration and computer hardware skills of the CICT Information Technology students. The said project was constructed using different computer peripheral parts. The CHSM Trainer has a dual core motherboard which has a built-in video card and a 512 RAM.
PDF Computer Hardware Servicing and Maintenance Trainer
The research focuses on creating a Computer Hardware Servicing and Maintenance trainer that will help the students better understand and develop the skills in computer hardware troubleshooting, connection and system upgrading. Fig. 3. The different Computer Hardware parts and connection of the trainer. It consists of the
computer hardware Latest Research Papers
This study introduces a screen-capture technology based approach to performative and authentic assessment. It involved design and implementation of screen-capture assignments to assess computer maintenance skills. The sample consisted of 28 students enrolled in computer hardware and software maintenance classes.
Effects of Computer-Based Training in Computer Hardware Servicing on
(DOI: 10.4018/ijtesss.317410) This study determined the effects of computer-based training in computer hardware servicing with a pedagogical agent named "DAC: The Builder" on the academic performance of computing students. Fifty-six university students (30 students in the control group, 26 students in the experimental group) participated in a two-week experiment. The majority of the ...
(PDF) COMPUTER HARDWARE SERVICING
In computer architecture, a bus (related to the Latin "omnibus", meaning "for all") is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols.
Information and Communications Technology Learner's Material Computer
Academia.edu is a platform for academics to share research papers. Information and Communications Technology Learner's Material Computer Hardware Servicing (PDF) Information and Communications Technology Learner's Material Computer Hardware Servicing | Charles Oliver Camba Livara - Academia.edu
Level of Competency in Computer Systems Servicing of Teachers in One
Advanced training in installing computer systems and networks, diagnose and troubleshoot computer system, configure computer systems and networks and maintain computer systems and networks which are the four core competency of TESDA's NC2 Computer Systems Servicing Course will be an extension program if there is a need for training.
Challenges in Computer System Servicing NCII
Academia.edu is a platform for academics to share research papers. Challenges in Computer System Servicing NCII . × ... ICT skills were related to internet connectivity,lack of software and hardware resources and lack of software and hardware resources and lack of practical skills. Aladejani & Alidanu (April 19,2018), finds that it reveals ...
PDF Learning Computer Hardware by Doing: Are Tablets Better Than Desktops?
International Journal of Research in Education and Science (IJRES), 2(1), 55-64. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.
(PDF) Information Technology Hardware Services based on ...
PDF | On Aug 1, 2021, Azham Bin Ahmad and others published Information Technology Hardware Services based on the Service Quality dimension to the IT Hardware Support Services: A Conceptual ...
Computer-based technology and student engagement: a ...
Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related ...
Today's computing challenges: opportunities for computer hardware
This paper reviews the technical challenges that today's computing systems are facing and introduces potential directions for continuous advancement of computing capability, and discusses where computer hardware designers find good opportunities to contribute. Keywords: Computer, Hardware, Silicon, Processor, Memory, Interconnect, Moore's ...
Research Paper About Computer Hardware Servicing
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Research Paper About Computer Hardware Servicing
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(PDF) Feasibility of Offering Bachelor of Technical ...
Based on CHEd Memorandum Order No. 79, series of 2017, the Bachelor of Technical Vocational Teacher Education (BTVTEd) Major in Computer Hardware Servicing program is designed to enhance the ...
COMPUTER HARDWARE SERVICING ICT-COMPUTER HARDWARE SERVICING
Major hardware components of a computer system The following list represents a basic set of hardware found in most PCs. 1. System Unit- The main part of a microcomputer, sometimes called the chassis. It includes the following parts: Motherboard, Microprocessor, Memory Chips, Buses, Ports, Expansion Slots and Cards.
Research Paper About Computer Hardware Servicing
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CHALLENGES IN COMPUTER SYSTEMS SERVICING NCII ...
Academia.edu is a platform for academics to share research papers. CHALLENGES IN COMPUTER SYSTEMS SERVICING NCII EXPERIENCED BY THE GRADE 12 STUDENTS IN THE SISTERS OF MARY SCHOOL-GIRLSTOWN ... It is a blanket term applied to the act of supporting and maintaining the computer hardware. Akhiar (2010) gave emphasis to a sound policy and holistic ...