course onlineAuthor: Tracey Tokuhama-Espinosa, Professor at Harvard University Extension School

Some of you have recently asked if there were any studies establishing the quality of online learning as compared with face-to-face. There is, and you can find several peer-reviewed articles to read for yourself in this Bundle: Online Education, Educational Technology, Gaming, AI-assisted Learning (click to expand list)

In sum, the evidence suggests six big ideas that should be taken into account before taking your classes online: (a) Teacher Factors; (b) Student Factors; (c) Quality Materials; (d) Communication; (e) Online Learning: Beyond “MOOCs”; and (f) change management. I give a brief summary of the literature below and then summarize with some recommendations, most importantly, the role of worked models and support systems.

Teacher Factors

First, online is a modality. This does not magically make a “bad” teacher a “good” one or vice versa. The quality of the teacher remains the single greatest determining factor in positive learning outcomes (Darling-Hammond et al., 2017). That is, it is not the tool or modality that matters, it is the competence of the instructor that determines if students report they learn well or not. Central to quality teaching is the teacher’s own self-efficacy, and even more important is the group self-efficacy (Hattie & Clark, 2018; McCarty, 2018). When multiple faculty members send the same message to the student about his or her potential to learn, the student’s own self-perception as a learner changes and outcomes are affected, for better or worse.

There is an inverse curve in educational levels and adaptation to what we know about modern learning; early childhood educators generally appear to embrace novelty, while university professors are often the most adverse to change (Tagg, 2012). There is generally a high level of self-critique and monitoring that occurs in the early childhood classroom and a relative openness to instructional novelty that appears to decrease on the part of teachers as students move into higher levels of education. Tertiary education has been the slowest to adjust to the demands of the modern learner, often ignoring progress in educational technology and discoveries about how the brain learns, despite the fact that these same discoveries are often made within universities themselves (Keeling & Hersh, 2016). Most university professors have never had a class on how to teach. While erudite in specialized fields of knowledge, few university professors know the benefits of one teaching tool or strategy over another and prioritize their life experience as a guide. This can have both positive and negative outcomes, though usually the latter as teachers do not up-date their pedagogical knowledge base.

Student Factors

Second, there is evidence that students’ attitudes (individually and collectively), as well as their self-perceptions as successful learners (or not) has one of the largest effect size impacts on outcomes (Hattie, 2012). While not the only factor, if students think they are not going to be successful, this leads to the self-fulfilling prophecy of failure (Zimmerman et al., 2017). On the other hand, if a student has a growth mindset (Dweck, 2006), an open attitude towards new experience, and believes that they can and will learn with the appropriate effort, then that student typically does well (Tang et al., 2019), which has also been shown in online settings (McClendon et al., 2017). This suggests that attitude, even more than aptitude, plays an important role in learning outcomes (Artlet, et al., 2003; Côté, 2000). In related research, there is strong evidence that self-regulation and other executive functioning skills account for almost double the impact of native intelligence related to learning outcomes (Duckworth, 2011; Moffitt, 2011). This means that being able to focus on a task and stick with it are more important than just being born smart.

Quality Materials

Third, online learning (including virtual classrooms) pose many challenges to both teachers and students in terms of materials because of choice. “Analysis paralysis” can occur when a person enters the virtual classroom as there are many more tools available to enhance the teaching-learning exchange in an online learning platform than in a face-to-face classroom (Crawford-Ferre, 2012). This is due in large part to the thousands of EdTech applications and plug-ins now available that work in large part to facilitate correction and rehearsal of the more technical or mechanical aspects of learning (i.e., checking for comma splices), leaving more time for teachers to spend on the human aspect of the teaching-learning process, such as taking the emotional pulse of learners and motivating them to reach their own potential in the subject.

Moreover, typical tools like quizzes, discussions, and small group work can be used for additional learning objectives. For example, self-corrected quizzes can be used to increase the amount of low-stakes testing that enhances memory (Grisson et al., 2011). As memory and attention are vital for learning, learning itself increases. This automated tool allows the instructor to spend more time on human-dependent teaching and learning strategies, such as helping students understand why they erred in their thinking processes.

Similarly, online discussions on a topic can be held over time asynchronously, which permits a dialogue to develop amongst learners, rather than being a single learner’s simple, solitary reflection. This means that in addition to hearing individual responses, a community of learners is constructed through the exchange which benefits perspective-taking, research, debate and overall communication skills.

Small-group work can benefit learning (Aldrich, 2016). Setting up such groups takes about 10-seconds to structure in an online videoconferencing context like Zoom’s break out rooms, whereas rearranging students in a physical classroom can take many minutes. The time savers on the organizing-end create additional time for deeper discussion and better use of contact time with the students.

Communication

Fourth, there is a strong argument that nothing replaces the face-to-face experience due to the human need for social contact (Hodas & Lerman, 2014). There is a large body of international evidence that shows that teacher-student relationships are highly influential in supporting student learning outcomes (Hattie & Anderman, 2013). This would suggest that the ability to “connect” plays a very important role in student motivation to learn, and consequently in outcomes. Despite the popular belief that being face-to-face is necessary for the benefits of socialization to be felt, there is research that shows that social contagion — the ability of an individual to “infect” others at the emotional level and spread to define the learning environment — is equally impactful in online contexts as it is in face-to-face contexts (Johnson, et al., 2011).

Others go so far as to suggest that social contagion is actually augmented in online settings because of member visibility (Bowers & Kumar, 2015). In synchronous online meetings, people see everyone’s faces all the time, whereas in a face-to-face-classroom people often have their backs to one another. The ability to see others’ faces speeds up the rate of social contagion experienced by the group. This means that an excited and motivated group becomes even more excited and motivated, but the opposite can also be true. This is why managing groups in virtual contexts takes on a magnified role. Good online classroom management is the key to leveraging social contagion in favor of the good learning environment. Furthermore, in videoconferencing settings, learners also see themselves, which makes them even more aware of their body language, facial expressions and overall communication towards the group. This enhanced self-awareness changes group dynamics, and if leveraged well by the instructor, can advance the group learning significantly.

There is an additional phenomena in online learning which is just beginning to be explored, and that is the disinhibition effect, or the ways online learning creates a level of “protection” and thus, a higher level of social engagement (Pytash & Ferdig, 2017). Authors suggest that learners are more willing to share ideas and engage with each other online because they feel protected by not being in a physically shared space (Salter, et al., 2017).

Online Learning: Beyond “MOOCs”

Fifth, “online” learning is a global concept that encompasses distinct learning experiences, ranging from MOOCs to webinars to virtual reality laboratories to K-12 supplemental classes to 100% online university classrooms. This broad range of sub-types of online learning makes it difficult to qualify what is meant by “taking your class online” as this could mean simply offering a holding repository for documents or a place to receive uploaded homework, to a “high-touch” life-altering technologically-assisted learning experience.

There is now a large body of evidence on the ineffectiveness of Massive Online Open Courses, or MOOCs (Mayer et al., 2020). MOOCs came onto the learning scene in force in the early 2000s and promised not only to democratize information at a low cost, but also captured the imagination of many educational leaders who quickly devised certification schemes through which income could be derived. The reality of MOOCs, and their effectiveness, however, is that they have an extremely low level of completion rate, require a very high level of self-control, and do not yet have an agreed upon evaluation scheme by which to measure student learning outcomes (Weinhardt & Sitzmann, 2019). While successful in democratizing open access to knowledge, MOOCs are far less successful in guaranteeing deep learning, have little or no contact from learner-to-learner or teacher-to-learner, and do not have a robust or universally agreed upon way of providing evidence of learning.

Not all online learning consists of MOOCs, despite their being the most studied format of online learning. Many people jump to the conclusion that “online learning is ineffective” and point to statistics of MOOC completion rates and/or the solitary nature and results of studying alone (Ejreaw & Drus, 2017). While MOOCs may not be an effective replacement for a course, other well-designed online experiences suggest the equal or even superior nature of online learning (Bazylak & Weiss, 2017).

Many universities around the world have invested heavily in the past decade to structure high quality learning experiences in the online modality leveraging the most up-to-date technology (He, et al., 2019). Good online design, similar to designing any learning experience face-to-face, requires extensive planning to leverage the right tools and strategies at the right time for the right audience.

Change Management

Sixth, it is natural for humans to fear change for two main reasons. From a neuroscientific perspective, this is because shifting from “go-to” heuristics and our innate biases towards new learning takes energy (Tokuhama-Espinosa, & Nelson, 2019). As the largest consumer of energy in the body organ-wise, the brain works hard to conserve whatever it can. It takes far less energy to complain about change than to imagine the possibilities of change, for example.

Second, fear is a strong motivator. People make decisions based on fear faster than on any other emotion (Bechara, et al., 2000). In evolutionary terms, this is easily explained by self-preservation: it is better to be fearful and possibly save yourself than to be at ease and be ambushed. It is much harder, but yields far more in terms of personal and collective growth, to take the time to exercise a cautiously optimistic openness. People who walk into situations of change well-prepared and having studied an issue in depth, make better decisions than people who are reactionary and who have nothing but anecdotal evidence (i.e.,”I failed at a MOOC, therefore I reject online learning”).

Worked Models, Templates and Decision Trees

One way to assure a smooth transition into the virtual classroom is through a worked model (Hattie, 2012). Most people moving online are intelligent, hardworking, and good at what they do, which is why feeling unintelligent, slow and bad at something frustrates them and leads them to label the move as “too hard.” One of the fastest ways to learn something new is when you have a worked model. When a person can see what a successful final product looks like, they are more likely to consider the task manageable (for a worked model of a Canvas course, see here). By simply sharing what a good virtual classroom or two looks like, most people reframe a hard task as now a manageable task and decide it is within their grasp.

Close to a worked model but different in purpose is a template, which is the basic design of an online course in which materials, like videos, discussion board questions, and quizzes can be populated with each teacher’s own content. This saves time but reduces creativity (for an example of a template in Moodle, write to info@conexiones.com.ec).

To help teachers make good decisions about what to put online and to give everyone a sense of “progress” towards a goal, the use of decision tree is helpful (for an example of a decision tree to create an online course, see here). Decision trees not only lay out a logical process towards the goal of opening online classrooms, but they also make people feel they have clear steps to take and well-defined tasks to complete.

Support Systems

Change is hard. Changing without support can feel impossible. When a change like going online into virtual classrooms is requested – or demanded – of a faculty with little or no warning, many people run through the same cycle of grief as if they had lost a loved one: 1. Denial and isolation; 2. Anger; 3. Bargaining; 4. Depression; 5. Acceptance. Access to a support system speeds up the cycle towards acceptance. In the rush to implement the software or purchase the hardware, many leaders forget the basic human need for clear communication. Simple, regular updates go a long way for lowering the angst of people who must implement the system. A highly stressed person does not learn; lowering the stress level of the group by attending to each individual is vital to getting a new system up and running quickly and efficiently. Students as well as teachers also need support. A quick welcome to the new online platform, even if simply filmed from your phone, personalizes the change and makes students feel that going online is manageable (for an example of a welcome video for students, see here).

Volunteers can be saviors at this point. No institution budgets for the need for a new department to hand-hold teachers going online in mass, but there are likely more resources at hand than you think. Other people more experienced in teaching online may be willing to be the support system for the new teachers. Students-helping-students is also an underused resource. Many who have managed to “survive” their own induction online are very happy to share tips with people new to the system. Use of teacher-to-teacher or student-to-student explanations make sense. After all, peer explanations are often even better than those from technical experts who might know the software but who have never taught in a virtual classroom before. And more often than not, the questions posed by people new to online platforms are easily answered by anyone who has even just slightly more experienced than them.

Summary

There is a vast amount of literature that points to the potentials, promises and perils of online learning. It is easy to demonstrate that online learning has several important advantages noted in rigorous comparative studies in which it was found that, “students in the online courses reported better understanding of the course structure, better communication with the course staff, watch[ed] the videos lessons more, and [had] higher engagement and satisfaction,” (Soffer & Nachmias, 2018, p. 534) than the same students in face-to-face classes.

Answers to educational challenges are never easy as there are multiple confounding variables that must be taken into consideration, not the least of which have to do with the quality of the teacher, the attitudes of the students, the use of instructional design and available tools, the personalization of the endeavor, the knowledge-base of the actors and the way change is managed by the institution.

The answers to our most frequent questions about online learning are naturally complex as human learning and the brain are complex. All animals learn, but few, including humans, have a science of teaching (Blakemore & Frith, 2005), and humans alone do this in modalities other than being face-to-face. Learning to teach online is not easy because it requires not only (a) knowing how to teach and (b) understanding the tools and strategies available, but it also presumes that teachers have (c) content or domain area knowledge (i.e., the history teacher knows history); and that the teacher has an (d) understanding of the brain and how humans learn best. None of these premises can be taken for granted. This means that to “take your course online,” educators and those requesting this change should offer the right training to fill in any potential gaps in teachers’ general abilities.

Online is a modality with a wonderful potential to save time (i.e., no more commuting to class), reach more students (i.e., classes are no longer limited to the physically present but can be comprised of global school houses and the ability to include those traditionally excluded from regular classrooms), differentiate (i.e., offer multiple levels of entry points to topics with a wider variety of resources), and personalize learning. This is all contingent, however, on ensuring both teachers and students are ready for the challenge and well-equipped with evidence-based premises before logging on.

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Parsons, D., Inkila, M., & Lynch, J. (2019). Navigating learning worlds: Using digital tools to learn in physical and virtual spaces. Australasian Journal of Educational Technology, 35(4).

Parsons, S. A., Hutchison, A. C., Hall, L. A., Parsons, A. W., Ives, S. T., & Leggett, A. B. (2019). US teachers’ perceptions of online professional development. Teaching and Teacher Education: An International Journal of Research and Studies, 82(1), 33-42.

Parsons, T. D., Lin, L., & Cockerham, D. (Eds.). (2018). Mind, brain and technology: Learning in the age of emerging technologies. Springer.

Pechenkina, E., & Aeschliman, C. (2017). What do students want? Making sense of student preferences in technology-enhanced learning. Contemporary Educational Technology, 8(1), 26-39.

Sandanayake, T. C. (2019). Promoting open educational resources-based blended learning. International Journal of Educational Technology in Higher Education, 16(1), 3.

Soffer, T., & Nachmias, R. (2018). Effectiveness of learning in online academic courses compared with face‐to‐face courses in higher education. Journal of Computer Assisted Learning, 34(5), 534-543.

Study.com (n.d.) Educational technology trends: What teachers should know. Available on https://study.com/academy/lesson/educational-technology-trends-what-teachers-should-know.html

Surabhi, S. (2019). 3 ways machine learning in EdTech is changing the educational industry. Net Solutions. [webpage] https://www.netsolutions.com/insights/machine-learning-in-edtech/

Technology for Every Student? (2:43 minutes) Interview with researcher Todd Rose about digital technology and Universal Design at the Center for Applied Special Technology (CAST).

Uncapher, M. R. (2018). Design considerations for conducting large‐scale learning research using innovative technologies in schools. Mind, Brain, and Education. https://doi.org/10.1111/mbe.12185

Universal Design for Learning (UDL) UDL is a set of principles for curriculum development that give all individuals equal opportunities to learn. UDL provides a blueprint for creating instructional goals, methods, materials, and assessments that work for everyone–not a single, one-size-fits-all solution but rather flexible approaches that can be customized and adjusted for individual needs.

Wilkerson, M. H. (2017). Teachers, students, and after-school professionals as designers of digital tools for learning. In Participatory Design for Learning (pp. 125-138). Routledge.

Williamson, B., Pykett, J., & Nemorin, S. (2018). Biosocial spaces and neurocomputational governance: brain-based and brain-targeted technologies in education. Discourse: Studies in the Cultural Politics of Education, 39(2), 258-275. [Request access from HOLLIS]

Witton, G. (2017). The value of capture: Taking an alternative approach to using lecture capture technologies for increased impact on student learning and engagement. British Journal of Educational Technology, 48(4), 1010-1019.

Xu, H., Liu, S., & Liu, M. (2019). Analysis of the application of modern educational technology in middle school mathematics teaching. International Journal of Innovation and Research in Educational Sciences, 6(3), 2349-5219.

Yen, S. C., Lo, Y., Lee, A., & Enriquez, J. (2018). Learning online, offline, and in-between: comparing student academic outcomes and course satisfaction in face-to-face, online, and blended teaching modalities. Education and Information Technologies, 23(5), 2141-2153.

Zhao, Y., Frey, B., Rice, S., Rury, J. L., & Isaacson, R. (2019). Investigating the Relationship between faculty perception of educational technology and the level of technology integration into teaching and learning (Doctoral dissertation, University of Kansas).

Instructional Design (Creation of Learning Environments)

Al Mamun, M. A., Lawrie, G., & Wright, T. (2020). Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers & Education, 144, 103695.

Alomyan, H., & Green, D. (2019, August). Learning theories: Implications for online learning design. In Proceedings of the 2019 3rd International Conference on E-Society, E-Education and E-Technology (pp. 126-130).

Baldwin, S. J., & Ching, Y. H. (2019). An online course design checklist: development and users’ perceptions. Journal of Computing in Higher Education, 31(1), 156-172.

Holland, A. A. (2019). Effective principles of informal online learning design: A theory-building metasynthesis of qualitative research. Computers & Education, 128, 214-226.

Ou, C., Joyner, D. A., & Goel, A. K. (2019). Designing and developing video lessons for online learning: A seven-principle model. Online Learning, 23(2), 82-104.

Powell, C. G., & Bodur, Y. (2019). Teachers’ perceptions of an online professional development experience: Implications for a design and implementation framework. Teaching and Teacher Education, 77, 19-30.

Suartama, I. K., Setyosari, P., Sulthoni, S., & Ulfa, S. (2019). Development of an instructional design model for mobile blended learning in higher education. International Journal of Emerging Technologies in Learning (iJET), 14(16), 4-22.

Educational Technology (Creation of stand-alone and complementary tools in formal and informal learning)

Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242.

Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: the absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 15(1), 14. [Request access from HOLLIS]

Bateman, B. L. (2019). Internet resources: Educational technology: A guide to resources on the Web. College & Research Libraries News, 64(1), 9-13.

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), 2.

Bond, M., Zawacki‐Richter, O., & Nichols, M. (2019). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 50(1), 12-63.

Brookings Institution. (2013). Education technology: The next generation. [ video ] (1:23:44 minutes). Available on Full Event – Education Technology: The Next Generation

Department of Education, U.S. (2014). Devices. Office of Educational Technology. [ video ] (2:34 minutes). Available on: Department of Education: Devices

Freina, L., & Ott, M. (2015, January). A literature review on immersive virtual reality in education: state of the art and perspectives. In The International Scientific Conference eLearning and Software for Education (Vol. 1, p. 133). Carol I National Defense University.

Gottschalk, F. (2019). Impacts of technology use on children: Exploring literature on the brain, cognition and well-being. Paris: OECD.

Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research?. British Journal of Educational Technology, 50(3), 956-971.

Hollands, F., & Escueta, M. (2019). How research informs educational technology decision-making in higher education: the role of external research versus internal research. Educational Technology Research and Development, 1-18.

Howard, M. C., & Gutworth, M. B. (2020). A meta-analysis of virtual reality training programs for social skill development. Computers & Education, 144, 103707.

Huang, R., Spector, J. M., & Yang, J. (2019). Educational Technology: A primer for the 21st centuary. Springer.

Ifenthaler, D., & Tracey, M. W. (2016). Exploring the relationship of ethics and privacy in learning analytics and design: implications for the field of educational technology. Educational Technology Research and Development, 64(5), 877-880.

Jacobs, K., Leopold, A., Hendricks, D. J., Sampson, E., Nardone, A., Lopez, K. B., … & Dembe, J. (2017). Project career: Perceived benefits of iPad apps among college students with Traumatic Brain Injury (TBI). Work, 58(1), 45-50.

Johnson, P., Anderson, A., & Cammidge, T. (2019). EdTech+ EdTeach: Exploring the Integration of Educational Technology Through Teacher Education. Edited by: Wafa Zoghbor, Suhair Al Alami, & Thomaï Alexiou, 71.

Kickmeier-Rust, M. D., Göbel, S., & Albert, D. (2008, September). 80Days: Melding adaptive educational technology and adaptive and interactive storytelling in digital educational games. In Proceedings of the First International Workshop on Story-Telling and Educational Games (STEG’08).

Lampert, B., Pongracz, A., Sipos, J., Vehrer, A., & Horvath, I. (2018). MaxWhere VR-learning improves effectiveness over clasiccal tools of e-learning. Acta Polytechnica Hungarica, 15(3), 125-147.

Liou, H. H., Yang, S. J., Chen, S. Y., & Tarng, W. (2017). The influences of the 2D image-based augmented reality and virtual reality on student learning. Journal of Educational Technology & Society, 20(3), 110-121.

London School of Economics. (2016). Edtech: The student view on educational technology. [ video ] (2:19 minutes). Available on: Edtech – The student view on educational technology

Maastricht University. (2018). Trend in educational technology. [webpage and videos]. https://library.maastrichtuniversity.nl/trends-in-educational-technology/

Olmos-Raya, E., Ferreira-Cavalcanti, J., Contero, M., Castellanos-Baena, M. C., Chicci-Giglioli, I. A., & Alcañiz, M. (2018). Mobile virtual reality as an educational platform: A pilot study on the impact of immersion and positive emotion induction in the learning process. In Eurasia Journal of Mathematics Science and Technology Education (Vol. 14, No. 6, pp. 2045-2057). Eurasia Publishing House.

Olmos, E., Cavalcanti, J. F., Soler, J. L., Contero, M., & Alcañiz, M. (2018). Mobile virtual reality: A promising technology to change the way we learn and teach. In Mobile and ubiquitous learning (pp. 95-106). Springer, Singapore. [Request access from HOLLIS]

Rankin, J. (2018). Teaching with educational technology. MIT OpenCourseWare. [ video ] (1:07:10 minutes). Available on: 8. Teaching with Educational Technology

Riva, G., Wiederhold, B. K., & Mantovani, F. (2019). Neuroscience of virtual reality: From virtual exposure to embodied medicine. Cyberpsychology, Behavior, and Social Networking, 22(1), 82-96.

Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of K-12 education. Journal of Education Policy, 31(4), 405-420.

Sahin, N. T., Abdus-Sabur, R., Keshav, N. U., Liu, R., Salisbury, J. P., & Vahabzadeh, A. (2018). Augmented Reality intervention for social communication in autism in a school classroom: Rated by teachers and parents as effective and usable in a controlled, longitudinal pilot study. https://doi.org/10.31234/osf.io/h2eu8

Sousa, M. J., Cruz, R., & Martins, J. M. (2017). Digital learning methodologies and tools–a literature review. Edulearn17 Proceedings, 5185-5192.

Spector, J. M., Ifenthaler, D., Sampson, D., Yang, J. L., Mukama, E., Warusavitarana, A., … & Bridges, S. (2016). Technology enhanced formative assessment for 21st century learning. Journal of Educational Technology & Society, 19(3), 58-71.

Spencer, K. (2017). The psychology of educational technology and instructional media. Routledge.

Gaming (Use of human thinking algorithms to reinforce learning)

Alstad, Z., Dahlstrom-Hakki, I., Asbell-Clarke, J., Rowe, E., & Altman, M. (2016). The use of multidimensional biopsychological markers to detect learning in educational gaming environments. Working Paper.

Bavelier, D. (2012). Your brain on video games. Ted Talk. [ video ] (17:15 minutes). Available on: https://www.ted.com/talks/daphne_bavelier_your_brain_on_video_games?language=en

Bediou, B., Adams, D. M., Mayer, R. E., Tipton, E., Green, C. S., & Bavelier, D. (2018). Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychological Bulletin, 144(1), 77.

Burgers, C., Eden, A., van Engelenburg, M. D., & Buningh, S. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94-103.

Chalki, P., Tsiara, A., & Mikropoulos, T. A. (2019). An educational neuroscience approach in the design of digital educational games. Themes in eLearning, 12, 17-34.

Charland, P., Allaire-Duquette, G., & Léger, P. M. (2018). Collecting neurophysiological data to investigate users’ cognitive states during game play. GSTF Journal on Computing (JoC), 2(3).

Churchill Club. (2012). Technology in education: How will it change the game? [ video ] (1:30:05 minutes). Available on: 5.7.12 Technology in education: How will it change the game?

Cohen, A. M. (2011). The gamification of education. The Futurist, 45(5), 16.

Cowley, B., Fantato, M., Jennett, C., Ruskov, M., & Ravaja, N. (2014). Learning when serious: Psychophysiological evaluation of a technology-enhanced learning game. Educational Technology & Society, 17(1), 3-16.

Deterding, S. (2012). Gamification: designing for motivation. Interactions, 19(4), 14-17.

Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011, May). Gamification. using game-design elements in non-gaming contexts. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 2425-2428). ACM.

Devonshire, I. M., Davis, J., Fairweather, S., Highfield, L., Thaker, C., Walsh, A., … & Hathway, G. J. (2014). Risk-based learning games improve long-term retention of information among school pupils. PloS One, 9(7), e103640.

Domínguez, A., Saenz-De-Navarrete, J., De-Marcos, L., FernáNdez-Sanz, L., PagéS, C., & MartíNez-HerráIz, J. J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380-392.

Dondlinger, M. J. (2007). Educational video game design: A review of the literature. Journal of Applied Educational Technology, 4(1), 21-31.

Erhel, S., & Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91, 106-114.

Gentry, S. V., Gauthier, A., Ehrstrom, B. L. E., Wortley, D., Lilienthal, A., Car, L. T., … & Car, J. (2019). Serious gaming and gamification education in health professions: systematic review. Journal of medical Internet research, 21(3), e12994.

Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in human behavior, 54, 170-179.

Hebert, S. (2018). The power of gamification education. [ video ] (18:48 minutes). TedTalk. Available on: The Power of Gamification in Education | Scott Hebert | TEDxUAlberta

Howard-Jones, P. (2013). Minds, brains and learning games at #LEGup. [ video ] (34:57 minutes). Available on: Dr Paul Howard-Jones on Minds, Brains and Learning Games at #LEGup

Howard-Jones, P. (2013). Plenary 3-Minds, brains and learning games.

Howard-Jones, P. A., Jay, T., Mason, A., & Jones, H. (2015). Gamification of learning deactivates the Default Mode Network Frontiers in Psychology, 6.

Kapp, K. M. (2012). The gamification of learning and instruction: game-based methods and strategies for training and education. Hoboken, NJ: John Wiley & Sons.

Kasemsap, K. (2016). Mastering educational computer games, educational video games, and serious games in the digital age. Gamification-Based E-Learning Strategies for Computer Programming Education, 30.

Kim, J. T., & Lee, W. H. (2015). Dynamical model for gamification of learning (DMGL). Multimedia Tools and Applications, 74(19), 8483-8493. [Request access from HOLLIS]

Koivisto, J., & Hamari, J. (2019). The rise of motivational information systems: A review of gamification research. International Journal of Information Management, 45, 191-210

Landers, R. N. (2014). Developing a theory of gamified learning linking serious games and gamification of learning. Simulation & Gaming, 45(6), 752-768.

Nelson, N. J., Fien, H., Doabler, C. T., & Clarke, B. (2016). Considerations for realizing the promise of educational gaming technology. Teaching Exceptional Children, 48(6), 293-300.

Ozcelik, E., Cagiltay, N. E., & Ozcelik, N. S. (2013). The effect of uncertainty on learning in game-like environments. Computers & Education, 67, 12-20.

Pozzi, F., Asensio-Perez, J. I., Ceregini, A., Dagnino, F. M., Dimitriadis, Y., & Earp, J. (2020). Supporting and representing learning design with digital tools: in between guidance and flexibility. Technology, Pedagogy and Education, 1-20.

Pynn, I. L. (2017). School has a bad storyline: Gamification in educational environments. Electronic Theses and Dissertations. 5652. University of Central Florida.

Ronimus, M., Kujala, J., Tolvanen, A., & Lyytinen, H. (2014). Children’s engagement during digital game-based learning of reading: The effects of time, rewards, and challenge. Computers & Education, 71, 237-246.

Sanger, J., Wilson, J., Davies, B., & Whittaker, R. (2019). Young children, videos and computer games: Issues for teachers and parents. Routledge.

Serrano-Laguna, Á., Manero, B., Freire, M., & Fernández-Manjón, B. (2018). A methodology for assessing the effectiveness of serious games and for inferring player learning outcomes. Multimedia Tools and applications, 77(2), 2849-2871.

Shi, L., Cristea, A. I., Hadzidedic, S., & Dervishalidovic, N. (2014, August). Contextual gamification of social interaction–towards increasing motivation in social e-learning. In International Conference on Web-Based Learning (pp. 116-122). Springer International Publishing.

Strickland, H. P., & Kaylor, S. K. (2016). Bringing your a-game: Educational gaming for student success. Nurse Education Today, 40, 101-103.

Thomas, A. (2018). The effective use of game-based education. TedTalk. [ video ] (17:08 minutes). Available on: The Effective Use of Game-Based Learning in Education | Andre Thomas | TEDxTAMU

Trajkovik, V., Malinovski, T., Vasileva-Stojanovska, T., & Vasileva, M. (2018). Traditional games in elementary school: Relationships of student’s personality traits, motivation and experience with learning outcomes. PloS one, 13(8).

Maddison, R., Simons, M., Straker, L., Witherspoon, L., Palmeira, A., & Thin, A. G. (2013). Active video games: An opportunity for enhanced learning and positive health effects?. Cognitive Technology, 18(1), 6-13.

Majuri, J., Koivisto, J., & Hamari, J. (2018). Gamification of education and learning: A review of empirical literature. In Proceedings of the 2nd International GamiFIN Conference, GamiFIN 2018. CEUR-WS.

Mayer, R. E. (2019). Computer games in education. Annual review of psychology, 70, 531-549.

McGonigal, J. (2010). Gaming can make a better world. Ted Talk. [ video ] (19:56 minutes).  Available on: https://www.ted.com/talks/jane_mcgonigal_gaming_can_make_a_better_world?language=en

Huizenga, J. C., Ten Dam, G. T. M., Voogt, J. M., & Admiraal, W. F. (2017). Teacher perceptions of the value of game-based learning in secondary education. Computers & Education, 110, 105-115.

Howard-Jones, P. (2012). Neuroscience, games & learning. [ video ] (29:26 minutes). Available on: Dr Paul Howard-Jones – Neuroscience, Games & Learning

Barata, G., Gama, S., Jorge, J., & Gonçalves, D. (2013, October). Improving participation and learning with gamification. In Proceedings of the First International Conference on gameful design, research, and applications (pp. 10-17). ACM.

Haskell, C. (2014). Blowing up the gradebook. [ video ] (17:44 minutes). Blowing up the gradebook – using video games for learning: Chris Haskell at TEDxAmmon

AI-assisted Learning

Ahlberg, S. (2018). N. Katherine Hayles, Unthought: The power of the cognitive nonconscious. Chicago and London: University of Chicago Press, 2017. 250 pages. ISBN-13: 978-0-226-44774-2 (cloth); 978-0-226-44788-9 (paper); 978-0-226-44791-9 (e-book). Studia Neophilologica, 90(2), 273-274.

Ahmadi, M., Borcea, C., & Jones, Q. (2019, March). Collaborative lifelogging through the integration of machine and human computation. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 23-24).

Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 1-13.

Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157-160.

Baker, M. J. (2000). The roles of models in Artificial Intelligence and Education research: a prospective view. Journal of Artificial Intelligence and Education, 11, 122-143..

Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A., Sponaugle, K., … & Bowser, R. (2018). Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathologica, 135(2), 227-247.

Bala, B. M. (2019). Artificial Intelligence and its Implications for Future. Artificial Intelligence.

Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., … & Ramamurthy, K. N. (2019). Think your artificial intelligence software is fair? Think again. IEEE Software, 36(4), 76-80.

Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018, March). Open learner models and learning analytics dashboards: a systematic review.  In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 41-50). ACM.

Bundy, A. (2017). Preparing for the future of Artificial Intelligence. Edinburgh Research Explorer.The University of Ediburgh.

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Qual Saf, 28(3), 231-237.

Chui, M. (2017). Artificial intelligence the next digital frontier?. McKinsey and Company Global Institute, 47, 3-6.

Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National bureau of economic research.

Conitzer, V. (2019). Designing preferences, beliefs, and identities for artificial intelligence. Durham, NC: Duke University. Association For the Advancement of Artificial Intelligence.

Conitzer, V., Sinnott-Armstrong, W., Borg, J. S., Deng, Y., & Kramer, M. (2017, February). Moral decision making frameworks for artificial intelligence. In Thirty-first aaai conference on artificial intelligence.

Crawford, K. (2016). Artificial intelligence’s white guy problem. The New York Times, 25.

D’Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., … & Alvarez-Jimenez, M. (2017). Artificial intelligence-assisted online social therapy for youth mental health. Frontiers in Psychology, 8, 796.

Das, A. K., Ashrafi, A., & Ahmmad, M. (2019, February). Joint cognition of both human and machine for predicting criminal punishment in judicial system. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 36-40). IEEE.

Dean, J. (2017). How will artificial intelligence affect your life. [ video ] (15:55 minutes).  Available on: How Will Artificial Intelligence Affect Your Life | Jeff Dean | TEDxLA

Deng, L. (2018). Artificial intelligence in the rising wave of deep learning: The historical path and future outlook [perspectives]. IEEE Signal Processing Magazine, 35(1), 180-177.

Deweerdt, S. (2019). Deep connections. Nature, 571(7766), S6-S8.

Dewey, D. (2013). The long-term future of AI (and what we can do about it). Ted Talk. (15:05 minutes)-[ video ]. Available on:The long-term future of AI(and what we can do about it): Daniel Dewey at TEDxVienna (Links to an external site.)Links to an external site.

Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349.

Ding, R. X., Palomares, I., Wang, X., Yang, G. R., Liu, B., Dong, Y., … & Herrera, F. (2020). Large-scale decision-making: characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion.

Eaton, E., Koenig, S., Schulz, C., Maurelli, F., Lee, J., Eckroth, J., … & Williams, T. (2018). Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters, 3(4), 23-31.

Fazal, M. I., Patel, M. E., Tye, J., & Gupta, Y. (2018). The past, present and future role of artificial intelligence in imaging. European Journal of Radiology, 105, 246-250.

Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216.

Frey, L. (2019). Artificial intelligence and integrated genotype–Phenotype identification. Genes, 10(1), 18.

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452.

Gil, Y., Honaker, J., Gupta, S., Ma, Y., D’Orazio, V., Garijo, D., … & Jahanshad, N. (2019, March). Towards human-guided machine learning. In Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 614-624).

Greene, D., Hoffmann, A. L., & Stark, L. (2019, January). Better, nicer, clearer, fairer: A critical Assessment of the movement for ethical artificial intelligence and machine learning. In Proceedings of the 52nd Hawaii International Conference on System Sciences.

Guestrin, C. (2019, October). 4 Systems perspectives into human-centered machine learning. In The 25th Annual International Conference on Mobile Computing and Networking (pp. 1-2).

Hager, G. D., Drobnis, A., Fang, F., Ghani, R., Greenwald, A., Lyons, T., … & Tambe, M. (2019). Artificial intelligence for social good (Links to an external site.)Links to an external site. arXiv preprint arXiv:1901.05406.

Haryanto, E., & Ali, R. M. (2019, January). Students’ attitudes towards the use of artificial intelligence SIRI in EFL learning at one public university. In International Seminar and Annual Meeting BKS-PTN Wilayah Barat (Vol. 1, No. 1).

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.

Hazard, C. J., Fusting, C., Resnick, M., Auerbach, M., Meehan, M., & Korobov, V. (2019). Natively interpretable machine learning and artificial intelligence: Preliminary results and future directions. arXiv preprint arXiv:1901.00246.

Helbing, D. (2019). Societal, economic, ethical and legal challenges of the digital revolution: from big data to deep learning, artificial intelligence, and manipulative technologies. In Towards Digital Enlightenment (pp. 47-72). Springer, Cham.

Homer, B. D., Ober, T. M., & Plass, J. L. (2018). Digital games as tools for embedded assessment. In A.A: Lipnevich & J.K. Smith’s The Cambridge handbook of instructional feedback, (pp. 357-375). Cambridge University Press.

Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S. J., & Lim, H. (2016). Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Computers & Education, 94, 18-36

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