Background

doi:10.1017/9781108894333.002

Part I - Background

Published online by Cambridge University Press: 19 November 2021

Edited by

Richard E. Mayer and

Logan Fiorella

Show author details

Richard E. Mayer: Affiliation:
University of California, Santa Barbara
Logan Fiorella: Affiliation:
University of Georgia

Book contents

Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Type: Chapter
Information: The Cambridge Handbook of Multimedia Learning , pp. 1 - 54

DOI: https://doi.org/10.1017/9781108894333.002 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.Google Scholar

Cognition and Technology Group at Vanderbilt. (1996). Looking at technology in context: A framework for understanding technology in education. In Berliner, D., & Calfee, R. C. (eds.), Handbook of Educational Psychology (pp. 807–840). New York: Macmillan.Google Scholar

Comenius, J. A. (1887). Orbis Pictus. Syracuse, NY: Bardeen. [Reproduced version.]Google Scholar

Cuban, L. (1986). Teachers and Machines: The Classroom Use of Technology Since 1920. New York: Teachers College Press.Google Scholar

Mayer, R. E. (2001). Changing conceptions of learning: A century of progress in the scientific study of education. In Corno, L. (ed.), Education across a Century: The Centennial Volume. One Hundredth Yearbook of the National Society for the Study of Education (pp. 34–75). Chicago, IL: University of Chicago Press.Google Scholar

Mayer, R. E. (2011). Applying the Science of Learning. Upper Saddle River, NJ: Pearson.Google Scholar

Mayer, R. E. (2014). Computer Games for Learning: An Evidence-Based Approach. Cambridge, MA: MIT Press.CrossRef Google Scholar

Mayer, R. E. (2019). Computer games in education. Annual Review of Psychology, 70, 531–549.Google Scholar

Mayer, R. E. (2021). Multimedia Learning (3rd ed.). New York: Cambridge University Press.Google Scholar

Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83, 484–490.CrossRef Google Scholar

Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444–452.Google Scholar

Norman, D. A. (1993). Things That Make Us Smart. Reading, MA: Addison-Wesley.Google Scholar

Paivio, A. (1986). Mental Representations: A Dual Coding Approach. Oxford: Oxford University Press.Google Scholar

Paivio, A. (2007). Mind and Its Evolution: A Dual Coding Theoretical Approach. Mahwah, NJ: Erlbaum.Google Scholar

Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representations. Learning and Instruction, 13, 141–156.Google Scholar

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.CrossRef Google Scholar

Thorndike, E. L. (1913). Educational Psychology. New York: Columbia University Press.Google Scholar

van Merrienboer, J. J. G., & Kirschner, P. A. (2013). Ten Steps to Complex Learning: A Systematic Approach to Four-Component Instructional Design (2nd ed.). New York: Routledge.Google Scholar

References

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. (eds.), The Psychology of Learning and Motivation (Vol. 2, pp. 89–195). New York: Academic Press.Google Scholar

Baddeley, A. (1986). Working Memory. Oxford: Oxford University Press.Google Scholar

Baddeley, A. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4, 417–423.CrossRef Google Scholar PubMed

Baddeley, A. (2012). Working memory, theories models and controversy. The Annual Review of Psychology, 63, 12.11–12.29.Google Scholar

Baddeley, A., & Andrade, J. (2000). Working memory and the vividness of imagery. Journal of Experimental Psychology: General, 129, 126–145.Google Scholar

Baddeley, A. D., Eysenck, M. W., & Anderson, M. C. (2020). Memory. New York: Psychology Press.Google Scholar

Baddeley, A., & Hitch, G. (1974). Working memory. In Bower, G. H. (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 8, pp. 47–89). New York: Academic Press.Google Scholar

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–170.Google Scholar

DeLeeuw, K. E., & Mayer, R. E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100, 223–234.Google Scholar

Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23, 1–19.Google Scholar

Kalyuga, S. (2014). The expertise reversal effect in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 576–597). New York: Cambridge University Press.Google Scholar

Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40(1), 1–17.Google Scholar

Kirschner, P. A., Park, B., Malone, S., & Jarodzka, H. (2017). Towards a cognitive theory of multimedia assessment (CTMMA). In Spector, M., Lockee, B. B., & Childress, M. D. (eds.), Learning, Design, and Technology. An International Compendium of Theory, Research, Practice, and Policy (1st ed., pp. 1–23). Cham, Switzerland: Springer International Publishing AG.Google Scholar

Kosslyn, S. M. (1980). Image and Mind. Cambridge, MA: Harvard University Press.Google Scholar

Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236.CrossRef Google Scholar

Mayer, R. E. (2014a). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 43–71). New York: Cambridge University Press.CrossRef Google Scholar

Mayer, R. E. (2014b). Introduction to multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 1–24). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2018). Educational psychology’s past and future contributions to the science of learning, science of instruction, and science of assessment. Journal of Educational Psychology, 110, 174–179.Google Scholar

Mayer, R. E. (2019). Computer games in education. Annual Review of Psychology, 70, 531–549.Google Scholar

Mayer, R. E. (2020). Multimedia Learning (3rd ed.). New York: Cambridge University Press.Google Scholar

Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.Google Scholar

Mayer, R. E., & Moreno, R. (1998). A cognitive theory of multimedia learning: Implications for design principles. In Naryanan, N. H. (ed.), Electronic Proceedings of the CHI’98 Workshop on Hyped-Media to Hyper-Media: Toward Theoretical Foundations of Design, Use and Evaluation. Available from www.researchgate.net/publication/248528255_A_Cognitive_Theory_of_Multimedia_Learning_Implications_for_Design_Principles (last accessed September 4, 2020).Google Scholar

Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological Review, 76(3), 241–263.Google Scholar

Paivio, A. (1975). Coding distinctions and repetition effects in memory. In Bower, G. H. (ed.), The Psychology of Learning and Motivation (Vol. 9, pp. 179–214). New York: Academic Press.Google Scholar

Paivio, A. (1990). Mental Representations: A Dual Coding Approach. New York: Oxford University Press.Google Scholar

Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287.Google Scholar

Sepp, S., Howard, S. J., Tindall-Ford, S., & Paas, F. (2019). Cognitive load theory and human movement: Towards an Integrated model of working memory. Educational Psychology Review, 31, 293–317.CrossRef Google Scholar

Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171, 701–703.Google Scholar

Skulmowski, A., & Rey, G. D. (2017a). Bodily effort enhances learning and metacognition: Investigating the relation between physical effort and cognition using dual-process models of embodiment. Advances in Cognitive Psychology, 13, 3–10.CrossRef Google Scholar PubMed

Skulmowski, A., & Rey, G. D. (2017b). Measuring cognitive load in embodied learning settings. Frontiers in Psychology, 8, 1191.Google Scholar

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.Google Scholar

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: SpringerGoogle Scholar

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.Google Scholar

Sweller, J., van Merrienboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292.Google Scholar

References

Abrami, P. C., Bernard, R. M., Borokhovski, E., Wade, A., Surkes, M. A., Tamin, R., & Zhang, D. (2008). Instructional interventions affecting critical thinking skills and dispositions: A stage 1 meta analysis. Review of Educational Research, 78(4), 1102–1134.Google Scholar

Ackerman, P. L. (2003). Cognitive ability and non-ability trait determinants of expertise. Educational Researcher, 32(8), 15–20.Google Scholar

Aksayli, N., Sala, G., & Gobet, F. (2019). The cognitive and academic benefits of CogMed: A meta-analysis. Educational Research Review, 27, 229–243.Google Scholar

Allcoat, D., & von Mühlenen, A. (2018). Learning in virtual reality: Effects on performance, emotion and engagement. Research in Learning Technology, 26, 1–13.CrossRef Google Scholar

Alpert, W. T., Couch, K. A., & Harmon, O. R. (2016). A randomized assessment of online learning. American Economic Review, 106(5), 378–382.CrossRef Google Scholar

Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4), 369–406.Google Scholar

Bagarukayo, E., Weide, T., Mbarika, V., & Kim, M. (2012). The impact of learning driven constructs on the perceived higher order cognitive skills improvement: Multimedia vs. text. International Journal of Education and Development using ICT, 8(2), 120–130.Google Scholar

Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637.Google Scholar

Barton, C. (ed.) (2019). The Research-ED Guide to Education Myths: An Evidence-Informed Guide for Teachers. Melton: John Catt Educational.Google Scholar

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, 77–110.Google Scholar

Berliner, D., & Glass, G. (eds.) (2014). 50 Myths & Lies That Threaten America’s Public Schools: The Real Crisis in Education. New York: Teachers College Press.Google Scholar

Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289.Google Scholar

Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., … & Huang, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379–439.Google Scholar

Bettinger, E. P., Fox, L., Loeb, S., & Taylor, E. S. (2017). Virtual classrooms: How online college courses affect student success. American Economic Review, 107(9), 2855–2875.Google Scholar

Borokhovski, E., Bernard, R. M., Tamim, R. M., Schmid, R. F., & Sokolovskaya, A. (2016). Technology-supported student interaction in post-secondary education: A meta-analysis of designed versus contextual treatments. Computers & Education, 96, 15–28.Google Scholar

Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2014). Interactive learning online at public universities: Evidence from a six‐campus randomized trial. Journal of Policy Analysis and Management, 33(1), 94–111.Google Scholar

Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218–237.Google Scholar

Calude, C., & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science, 22, 595–612.Google Scholar

Cambridge English Dictionary (n.d.). Principle. Available from https://dictionary.cambridge.org/us/dictionary/english/ (last accessed April 2021).Google Scholar

Chen, Z. (2012). We care about you: Incorporating pet characteristics with educational agents through reciprocal caring approach. Computers & Education, 59, 1081–1088.Google Scholar

Cheng, L., Ritzhaupt, A. D., & Antonenko, P. (2019). Effects of the flipped classroom instructional strategy on students’ learning outcomes: A meta-analysis. Educational Technology Research and Development, 67(4), 793–824.Google Scholar

Christodoulou, D. (2014). Seven Myths about Education. New York: Routledge.Google Scholar

Clark, R. E. (1982). Antagonism between achievement and enjoyment in ATI studies. Educational Psychologist, 17(2), 92–101.CrossRef Google Scholar

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.CrossRef Google Scholar

Clark, R. E. (1989). When teaching kills learning: Research on mathemathantics. In Mandl, H., De Corte, E., Bennett, N., & Friedrich, H. F. (eds.), Learning and Instruction. European Research in an International Context. Volume II. Oxford: Pergamon.Google Scholar

Clark, R. E. (2009). How much and what type of guidance is optimal for learning from instruction? In Tobias, S. and Duffy, T. M. (eds.), Constructivist Theory Applied to Instruction: Success or Failure? (pp. 158–183). New York: Taylor & Francis.Google Scholar

Clark, R. E. (2012) Learning from Media: Arguments, Analysis and Evidence (2nd ed.). Greenwich, CT: Information Age Publishing.Google Scholar

Clark, R. E., & Choi, S. (2005). Five design principles for experiments on the effects of animated pedagogical agents. Journal of Educational Computing Research, 32(3), 209–225.Google Scholar

Clark, R. E., & Feldon, D. F. (2005). Five common but questionable principles of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 97–115). New York: Cambridge University Press.CrossRef Google Scholar

Clark, R. E., & Feldon, D. F. (2014). Ten common but questionable principles of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 151–173). New York: Cambridge University Press.Google Scholar

Clark, R. E., Howard, K., & Early, S. (2006). Motivational challenges experienced in highly complex learning environments. In Elen, J., & Clark, R. E. (eds.), Handling Complexity in Learning Environments: Theory and Research (pp. 27–43). Oxford: Elsevier.Google Scholar

Clark, R. E., Kirschner, P. A., & Sweller, J. (2012). Putting students on the path to learning: The case for fully guided instruction. American Educator, 36(1), 6–11.Google Scholar

Clark, R. E., & Saxberg, B. (2012). The “active ingredients” approach to the development and testing of evidence-based instruction by instructional designers. Educational Technology, 52(5), 20–25.Google Scholar

Clark, R. E., & Saxberg, B. (2018). Engineering motivation using the belief–expectancy–control framework. Interdisciplinary Education and Psychology, 2(1), 4–32.Google Scholar

Clark, R. E., & Saxberg, B. (2019, March). 4 Reasons Good Employees Lose Their Motivation. Harvard Business Review. Available from https://hbr.org/2019/03/4-reasons-good-employees-lose-their-motivation?autocomplete=true (last accessed April 21, 2021).Google Scholar

Cronbach, L., & Snow, R. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Halsted Press.Google Scholar

Cuban, L. (1986). Teachers and Machines: The Classroom Use of Technology since 1920. New York: Teachers College Press.Google Scholar

Davis, R. O. (2018). The impact of pedagogical agent gesturing in multimedia learning environments: A meta-analysis. Educational Research Review, 24, 193–209.Google Scholar

De Bruyckere, P., Kirschner, P., & Hulshof, C. (2015). Urban Myths about Learning and Education. Waltham, MA: Academic Press.Google Scholar

De Bruyckere, P., Kirschner, P. A., & Hulshof, C. (2019). More Urban Myths about Learning and Education: Challenging Eduquacks, Extraordinary Claims, and Alternative Facts. New York: Routledge.Google Scholar

DeKeyser, R. M. (2003). Implicit and explicit learning. In Doughty, C., & Long, M. (eds.), The Handbook of Second Language Acquisition (pp. 313–348). Oxford: Blackwell.Google Scholar

Dembo, M. H., & Howard, K. (2007). Advice about the use of learning styles: A major myth in education. Journal of College Reading and Learning, 37(2), 101–109.Google Scholar

Dengel, A., & Mägdefrau, J. (2019). Presence is the key to understanding immersive learning. In Beck, D., Pena-Rios, A., Ogle, T., Economou, D., Mentzelopoulos, M., Morgado, L., Eckhardt, C., Pirker, J., Koitz-Hristov, R., Richter, J., Gutl, C., & Gardner, M. (eds.), Immersive Learning Research Network. iLRN 2019. Communications in Computer and Information Science (Vol. 1044, pp. 185–198). Cham: Springer.Google Scholar

Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining “gamification.” In Lugmayr, A., Franssila, H., Safran, C., & Hammouda, I. (eds.), MindTrek 2011 (pp. 9–15). New York: ACM.Google Scholar

Dichev, C., & Dicheva, D. (2017). Gamifying education: What is known, what is believed and what remains uncertain: A critical review. International Journal of Educational Technology in Higher Education, 14(1), 9.Google Scholar

Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Educational Technology & Society, 18(3), 75–88.Google Scholar

Domagk, S. (2010). Do pedagogical agents facilitate learner motivation and learning outcomes?: The role of the appeal of agent’s appearance and voice. Journal of Media Psychology: Theories, Methods, and Applications, 22(2), 84–97.CrossRef Google Scholar

Domagk, S., Schwarz, R. N., & Plass, J. L. (2010) Interactivity in multimedia learning: An integrated model. Computers and Human Behavior, 25(1), 1024–1033.Google Scholar

Dovis, S., van Rentergem, J., & Huizenga, H. (2015). Does CogMed working memory training really improve inattention in daily life? A Reanalysis. PLoS ONE, 10(3), e0119522.Google Scholar

Duffy, T. M., & Jonassen, D. H. (eds.) (1992). Constructivism and the Technology of Instruction, a Conversation. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Dunn, R, & Dunn, K. (1978). Teaching Students through Their Individual Learning Styles: A Practical Approach. Reston, VA: Reston Publishing Company.Google Scholar

Elfeky, A. I. M. (2019). The effect of personal learning environments on participants’ higher order thinking skills and satisfaction. Innovations in Education and Teaching International, 56(4), 505–516.Google Scholar

Facione, P. A. (1990). The California Critical Thinking Skills Test – College Level: Interpreting the CCTST, Group Norms and Sub-scores (Technical Report No. 4). Millbrae: California Academic Press.Google Scholar

Faiella, F., & Ricciardi, M. (2015). Gamification and learning: A review of issues and research. Journal of e-Learning and Knowledge Society, 11(3), 1–12.Google Scholar

Ferdig, R., Baumgartner, E., Hartshorne, R., Kaplan-Rakowski, R., & Mouza, C. (eds.) (2020). Teaching, Technology, and Teacher Education during the COVID-19 Pandemic: Stories from the Field. Waynesville, NC: Association for the Advancement of Computing in Education.Google Scholar

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

Fontana, L. A., Dede, C., White, C. S., & Cates, W. M. (1993). Multimedia: A Gateway to Higher-Order Thinking Skills. Fairfax, VA: George Mason University, Center for Interactive Educational Technology.Google Scholar

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 13–144.Google Scholar

Gillette, C., Rudolph, M., Kimble, C., Rockich-Winston, N., Smith, L., & Broedel-Zaugg, K. (2018). A meta-analysis of outcomes comparing flipped classroom and lecture. American Journal of Pharmaceutical Education, 82(5), Article 6898.Google Scholar

Gredler, M., & Shields, C. (2004). Does no one read Vygotsky’s words? Commentary on Glassman. Educational Researcher, 33(2), 21–25.Google Scholar

Gulikers, J. T. M., Bastiaens, T. J., & Martens, R. L. (2005). The surplus value of an authentic learning environment. Computers in Human Behavior, 21(3), 509–521.Google Scholar

Heeter, C. (1992). Being there: The subjective experience of presence. Presence: Teleoperators and Virtual Environments, 1(2), 262–271.CrossRef Google Scholar

Herrington, J., & Kervin, L. (2007). Authentic learning supported by technology: Ten suggestions and cases of integration in classrooms. Educational Media International, 44(3), 219–236.Google Scholar

Herrington, J., Reeves, T. C., and Oliver, R. (2014) Authentic learning environments. In Spector, J., Merrill, M., Elen, J., & Bishop, M. (eds.), Handbook of Research on Educational Communications and Technology (pp. 401–412). New York: Springer.Google Scholar

Homer, B., Plass, J., & Blake, L. (2008). The effects of video on cognitive load and social presence in multimedia-learning. Computers in Human Behavior, 34, 786–797.Google Scholar

Husmann, P. R., & O’Loughlin, V. D. (2018). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical Sciences Education, 12, 6–19.Google Scholar

James, W. B., & Gardner, D. L. (1995). Learning styles: Implications for distance learning. New Directions for Adult and Continuing Education, 67, 19–31.Google Scholar

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.Google Scholar

Karbach, J., & Verhaeghen, P. (2014). Making working memory work: A meta-analysis of executive-control and working memory training in older adults. Psychological Science, 25(11), 2027–2037.Google Scholar

Karich, A. C., Burns, M. K., & Maki, K. E. (2014). Updated meta-analysis of learner control within educational technology. Review of Educational Research, 84(3), 392–410.Google Scholar

Kassai, R., Futo, J., Demetrovics, Z., & Takacs, Z. K. (2019). A meta-analysis of the experimental evidence on the near- and far-transfer effects among children’s executive function skills. Psychological Bulletin, 145(2), 165–188.Google Scholar

Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jimenez, L., Brown, J., & Mackintosh, N. (2010). Implicit learning as an ability. Cognition, 116(3), 321–340.Google Scholar

Khacharem, A., Zoudji, B., & Kalyuga, S. (2015). Expertise reversal for different forms of instructional designs in dynamic visual representations. British Journal of Educational Technology, 46(4), 756–767.Google Scholar

Kim, Y., Thayne, J., & Wei, Q. (2017). An embodied agent helps anxious students in mathematics learning. Educational Technology Research and Development, 65(1), 219–235.Google Scholar

Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171.Google Scholar

Kirschner, P. A., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching. Educational Psychologist, 41, 75–86.CrossRef Google Scholar

Koedinger, K. R., & Aleven, V. (2007). The assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19, 239–264.Google Scholar

Kozma, R. (1994) Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42(2), 7–19.Google Scholar

Kramer, N. C., & Bente, G. (2010) Personalizing e-learning: The social effects of pedagogical agents. Educational Psychology Review, 22(1), 71–87.Google Scholar

Krassmann, A., Melo, M., Peixoto, B., Pinto, D., Bessa, M., & Bercht, M. (2020). Learning in virtual reality: Investigating the effects of immersive tendencies and sense of presence. In Chen, J. Y. C., & Fragomeni, G. (eds.), International Conference on Human–Computer Interaction (HCII 2020, Lecture Notes in Computer Science (Vol. 12191, pp. 270–286). Cham: Springer.Google Scholar

Kyllonen, P. C., & Lajoie, S. P. (2003). Reassessing aptitude: Introduction to a special issue in honor of Richard E. Snow. Educational Psychologist, 38(2), 79–83.Google Scholar

Landers, R. N., & Reddock, C. M. (2017). A meta-analytic investigation of objective learner control in web-based instruction. Journal of Business and Psychology, 32(4), 455–478.Google Scholar

Lilienfeld, S. (2017). Psychology’s replication crisis and the grant culture: Righting the ship. Perspectives on Psychological Science, 12, 660–664.CrossRef Google Scholar PubMed

Lohman, D. F. (1986). Predicting mathemathantic effects in the teaching of higher-order thinking skills, Educational Psychologist, 21(3), 191–208.Google Scholar

Ma, J., & Nickerson, J. V. (2006). Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Computing Surveys, 38(3), 1–14.Google Scholar

Makin, S. (2016). Memory games. Nature, 531, S10–S11.Google Scholar

Makransky, G., Borre‐Gude, S., & Mayer, R. E. (2019). Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. Journal of Computer Assisted Learning, 35(6), 691–707.Google Scholar

Mayer, R. (2001). What good is educational psychology? The case of cognition and instruction. Educational Psychologist, 36(2), 83–88.Google Scholar

Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19.Google Scholar

Mayer, R. E., & Chandler, P. (2001) When learning is just a click away: Does simple user interaction foster a deeper understanding of multimedia messages? Journal of Educational Psychology, 94(2), 390–397.Google Scholar

Merrill, D. M. (2006). Hypothesized performance on complex tasks as a function of scaled instructional strategies. In Elen, J., & Clark, R. E. (eds.), Handling Complexity in Learning Environments: Research and Theory (pp. 265–282). Oxford: Elsevier Science.Google Scholar

Moos, D. C., & Marroquin, E. (2010). Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior, 26, 265–276.Google Scholar

Nancekivell, S. E., Shah, P., & Gelman, S. A. (2020). Maybe they’re born with it, or maybe it’s experience: Toward a deeper understanding of the learning style myth. Journal of Educational Psychology, 112(2), 221–235.Google Scholar

Neelen, M., & Kirschner, P. (2020). Evidence-Informed Learning Design: Creating Training to Improve Performance. London: Kogan Page.Google Scholar

Newton, P. M., & Miah, M. (2017). Evidence-based higher education – Is the learning styles ‘myth’ important? Frontiers in Psychology, 8, 444–454.Google Scholar

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), 1–8.Google Scholar

Papert, S. (1987). Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30.Google Scholar

Park, S. (2015). The effects of social cue principles on cognitive load, situational interest, motivation, and achievement in pedagogical agent multimedia learning. Educational Technology & Society, 19(4), 211–229.Google Scholar

Parong, J., & Mayer, R. (2021). Cognitive and affective processes for learning science in immersive virtual reality. Journal of Computer Assisted Learning, 37, 83–98.Google Scholar

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119.Google Scholar

Picciano, A. (2002). Beyond student perceptions: Issues of interaction, presence, and performance in an online course. Journal of Asynchronous Learning Networks, 6(1), 21–40.Google Scholar

Post, L. S., Guo, P., Saab, N., & Admiraal, W. (2019). Effects of remote labs on cognitive, behavioral, and affective learning outcomes in higher education. Computers & Education, 140, 103596.Google Scholar

Redick, T. (2015). Working memory training and interpreting interactions in intelligence interventions. Intelligence, 50, 14–20.Google Scholar

Reich, J., Buttimer, C. J., Fang, A., Hillaire, G., Hirsch, K., Larke, L., Littenberg-Tobias, J., Moussapour, R., Napier, A., Thompson, M., & Slama, R. (2020). Remote Learning Guidance from State Education Agencies during the COVID-19 Pandemic: A First Look. Available from https://edarxiv.org/437e2 (last accessed April 21, 2021).Google Scholar

Rey, G. D., Beege, M., Nebel, S., Wirzberger, M., Schmitt, T. H., & Schneider, S. (2019). A meta-analysis of the segmenting effect. Educational Psychology Review, 31, 389–419.Google Scholar

Richardson, J., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402–417.Google Scholar

Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32, 77–112.Google Scholar

Sala, G., Aksayli, N., Tatlidil, K., Tatsumi, T., Gondo, Y., & Gobet, F. (2019). Near and far transfer in cognitive training: A second-order meta-analysis. Collabra: Psychology, 5(1), art.18.Google Scholar

Sala, G., & Gobet, F. (2016). Do the benefits of chess instruction transfer to academic and cognitive skills? A meta-analysis. Educational Research Review, 18, 46–57.Google Scholar

Sala, G., & Gobet, F. (2020). Working memory training in typically developing children: A multilevel meta-analysis. Psychonomic Bulletin & Review, 27, 423–434.Google Scholar

Sala, G., Tatlidil, K. S., & Gobet, F. (2018). Video game training does not enhance cognitive ability: A comprehensive meta-analytic investigation. Psychological Bulletin, 144(2), 111–139.Google Scholar

Salomon, G. (1984). Television is “easy” and print is “tough”: The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology, 76(4), 647–658.Google Scholar

Savery, J. R., & Duffy, T. M. (2001). Problem Based Learning: An Instructional Model and Its Constructivist Framework (CRLT Technical Report 16-01). Bloomington, IN: Center for Research on Learning and Technology.Google Scholar

Scheibe, C., & Rogow, F. (2012). The Teachers Guide to Media Literacy: Critical Thinking in a Multimedia World. Thousand Oaks, CA: Corwin Press.Google Scholar

Schmidt, F. L., & Oh, I. S. (2013). Methods for second order meta-analysis and illustrative applications. Organizational Behavior and Human Decision Processes, 121(2), 204–218.Google Scholar

Schrader, C., & Bastiaens, T. (2012). The influence of virtual presence: Effects on experienced cognitive load and learning outcomes in educational computer games. Computers in Human Behavior, 28, 648–658.Google Scholar

Schroeder, N. L., & Gotch, C. M. (2015). Persisting issues in pedagogical agent research. Journal of Educational Computing Research, 53(2), 183–204.Google Scholar

Schunk, D. H., Pintrich, P. R., & Meece, J., L. (2008). Motivation in Education (3rd ed.). Upper Saddle River, NJ: Pearson Merrill Prentice Hall.Google Scholar

Schwaighofer, M., Fischer, F., & Bühner, M. (2015). Does working memory training transfer? A meta-analysis including training conditions as moderators. Educational Psychologist, 50, 138–166.Google Scholar

Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of Human–Computer Studies, 74, 14–31.Google Scholar

Selwyn, N. (2013). Education in a Digital World: Global Perspectives on Technology and Education. New York: Routledge.Google Scholar

Shulman, L. S. (1970). Reconstruction of educational research. Review of Educational Research, 40(3), 371–396.Google Scholar

Shulman, L. S. (1986). Paradigms and research programs in the study of teaching: A contemporary perspective. In Wittrock, M. C. (ed.), Handbook of Research on Teaching (3rd ed., pp. 3–36). New York: Macmillan.Google Scholar

Shulman, L. S., & Quinlan, S. S. (1996). The comparative psychology of school subjects. In Berliner, D. C., & Calfee, R. C. (eds.), Handbook of Educational Psychology (pp. 399–422). New York: Macmillan.Google Scholar

Simons, D., Boor, W., Charness, N., Gathercole, S., Chabris, C., Hambrick, D., & Stine-Morrow, E. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186.Google Scholar

Spencer-Smith, M., & Klingberg, T. (2015). Benefit of a working memory training program for inattention in daily life: A systematic review and meta-analysis. PLoS ONE, 10(3), e0119522.Google Scholar

Sternberg, R. J., Grigorenko, E. L., & Kidd, K. K. (2005). Intelligence, race, and genetics. American Psychologist, 60(1), 46–59.Google Scholar

Stevens, R., Wineburg, S., Rupert Herrenkohl, L., & Bell, P. (2005). Comparative understanding of school subjects: Past, present, future. Review of Educational Research, 75(2), 125–157.Google Scholar

Stoney, S., & Oliver, R. (1999) Can higher order thinking and cognitive engagement be enhanced with multimedia. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning. Accessed from: http://imej.wfu.edu/articles/1999/2/07/index.asp (last accessed April 2021).Google Scholar

Sung, E., & Mayer, R. E. (2013) Online multimedia learning with mobile devices and desktop computers: An experimental test of Clark’s methods-not-media hypothesis. Computers in Human Behavior, 29, 639–647.Google Scholar

Sweller, J. (2008). Instructional implications of David C. Geary’s evolutionary educational psychology, Educational Psychologist, 43(4), 214–216.Google Scholar

Tobias, S., & Duffy, T. M. (eds.) (2009). Constructivist Instruction: Success or Failure. New York: Routledge.Google Scholar

Triona, L. M., & Klahr, D. (2003). Point and click or grab and heft: Comparing the influence of physical and virtual instructional materials on elementary students ability to design experiments. Cognition and Instruction, 21(2), 149–173.Google Scholar

Turlik, M. (2009). Evaluating the results of a systematic review/meta-analysis. The Foot and Ankle Online Journal, 2(7), 5.Google Scholar

Valsiner, J. (1988). Developmental Psychology in the Soviet Union. Bloomington: Indiana University Press.Google Scholar

van Dijk, J. (2020). The Digital Divide. Medford, MA: Polity Press.Google Scholar

VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539.Google Scholar

Wang, F., Li, W., Mayer, R. E., & Liu, H. (2018). Animated pedagogical agents as aids in multimedia learning: Effects on eye-fixations during learning and learning outcomes. Journal of Educational Psychology, 110(2), 250–268.Google Scholar

Wang, Y. (2016). Big opportunities and big concerns of big data in education. TechTrends, 60, 381–384.Google Scholar

Wiesner, T. F., & Lan, W. (2004). Comparison of student learning in physical and simulated unit operations experiments. Journal of Engineering Education, 93(3), 195–204.Google Scholar

Wilson, L. C. (2014, September). Introduction to meta-analysis: A guide for the novice. Available from www.psychologicalscience.org/observer/introduction-to-meta-analysis-a-guide-for-the-novice (last accessed April 21, 2021).Google Scholar

Wise, A., Chang, J., Duffy, T., & Del Valle, R. (2004). The effects of teacher social presence on student satisfaction, engagement, and learning. Journal of Educational Computing Research, 31, 247–271.Google Scholar

Wise, A., & Shaffer, D. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 2–13.Google Scholar

Yung, H. I., & Paas, F. (2015). Effects of cueing by a pedagogical agent in an instructional animation: A cognitive load approach. Educational Technology & Society, 18(3), 153–160.Google Scholar

Zacharia, Z. C., & Constantinou, C. P. (2008). Comparing the influence of physical and virtual manipulatives in the context of the physics by inquiry curriculum: The case of undergraduate students’ conceptual understanding of heat and temperature. American Journal of Physics, 76(4), 425–430.Google Scholar

References

Ahern, S., & Beatty, J. (1979). Pupillary responses during information processing vary with scholastic aptitude test scores. Science, 205(4412), 1289–1292.Google Scholar

Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428.Google Scholar

Amadieu, F., van Gog, T., Paas, F., Tricot, A., & Mariné, C. (2009). Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning. Learning and Instruction, 19(5), 376–386.Google Scholar

Anmarkrud, Ø., Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61–83.Google Scholar

Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.Google Scholar

Argelagós, E., Brand-Gruwel, S., Jarodzka, H., & Pifarré, M. (2018). Unpacking cognitive skills engaged in web-search: How can log files, eye movements, and cued-retrospective reports help? An in-depth qualitative case study. International Journal of Innovation and Learning, 24(2), 152–175.CrossRef Google Scholar

Arslan-Ari, I., Crooks, S. M., & Ari, F. (2020). How much cueing is needed in instructional animations? The role of prior knowledge. Journal of Science Education and Technology, 29(5), 666–676.Google Scholar

Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389–400.Google Scholar

Baars, M., van Gog, T., de Bruin, A., & Paas, F. (2018). Accuracy of primary school children’s immediate and delayed judgments of learning about problem-solving tasks. Studies in Educational Evaluation, 58, 51–59.Google Scholar

Baars, M., Wijnia, L., de Bruin, A., & Paas, F. (2020). The relation between student’s effort and monitoring judgments during learning: A meta-analysis. Educational Psychology Review, 32, 979–1002.Google Scholar

Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1–29.Google Scholar

Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185.Google Scholar

Beege, M., Ninaus, M., Schneider, S., Nebel, S., Schlemmel, J., Weidenmüller, J., Moeller, K., & Rey, G. D. (2020). Investigating the effects of beat and deictic gestures of a lecturer in educational videos. Computers & Education, 156, 103955.Google Scholar

Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A., & Montanari, R. (2011). Driver workload and eye blink duration. Transportation Research Part F: Traffic Psychology and Behaviour, 14(3), 199–208.Google Scholar

Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student–teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401–411.Google Scholar

Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, 411–417.Google Scholar

Blayney, P., Kalyuga, S., & Sweller, J. (2016). The impact of complexity on the expertise reversal effect: Experimental evidence from testing accounting students. Educational Psychology, 36(10), 1868–1885.Google Scholar

Bokosmaty, S., Sweller, J., & Kalyuga, S. (2015). Learning geometry problem solving by studying worked examples: Effects of learner guidance and expertise. American Educational Research Journal, 52(2), 307–333.Google Scholar

Boucheix, J.-M., Gauthier, P., Fontaine, J.-B., & Jaffeux, S. (2018). Mixed camera viewpoints improve learning medical hand procedure from video in nurse training? Computers in Human Behavior, 89, 418–429.Google Scholar

Brand-Gruwel, S., Kammerer, Y., van Meeuwen, L., & van Gog, T. (2017). Source evaluation of domain experts and novices during Web search: Evaluation of sources. Journal of Computer Assisted Learning, 33(3), 234–251.Google Scholar

Brucker, B., Ehlis, A.-C., Häußinger, F. B., Fallgatter, A. J., & Gerjets, P. (2015). Watching corresponding gestures facilitates learning with animations by activating human mirror-neurons: An fNIRS study. Learning and Instruction, 36, 27–37.Google Scholar

Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor-Analytic Studies. Cambridge: Cambridge University Press.Google Scholar

Chan, K. Y., Lyons, C., Kon, L. L., Stine, K., Manley, M., & Crossley, A. (2020). Effect of on-screen text on multimedia learning with native and foreign-accented narration. Learning and Instruction, 67, 101305.Google Scholar

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Thousand Oaks, CA: Sage Publication.Google Scholar

Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide, The Journal of the Learning Sciences, 6(3), 271–315.Google Scholar

Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar

Chisari, L. B., Mockevičiūtė, A., Ruitenburg, S. K., Vemde, L., Kok, E. M., & Gog, T. (2020). Effects of prior knowledge and joint attention on learning from eye movement modelling examples. Journal of Computer Assisted Learning, 36(4), 569–579.Google Scholar

Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324.Google Scholar

Corsi, P. M. (1972). Human Memory and the Medial Temporal Region of the Brain. Montreal, QC: McGill University.Google Scholar

Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26(2), 197–223.Google Scholar

Cristino, F., Mathôt, S., Theeuwes, J., & Gilchrist, I. D. (2010). ScanMatch: A novel method for comparing fixation sequences. Behavior Research Methods, 42(3), 692–700.Google Scholar

Daneman, M., & Carpenter, P. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466.Google Scholar

de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134.Google Scholar

de Koning, B. B., Hoogerheide, V., & Boucheix, J.-M. (2018). Developments and trends in learning with instructional video. Computers in Human Behavior, 89, 395–398.Google Scholar

de Koning, B. B., Marcus, N., Brucker, B., & Ayres, P. (2019). Does observing hand actions in animations and static graphics differentially affect learning of hand-manipulative tasks? Computers & Education, 141, 103636.Google Scholar

de Koning, B. B., Rop, G., & Paas, F. (2020a). Learning from split-attention materials: Effects of teaching physical and mental learning strategies. Contemporary Educational Psychology, 61, 101873.Google Scholar

de Koning, B. B., Rop, G., & Paas, F. (2020b). Effects of spatial distance on the effectiveness of mental and physical integration strategies in learning from split-attention examples. Computers in Human Behavior, 110, 106379.Google Scholar

Demaree, D., Jarodzka, H., Brand-Gruwel, S., & Kammerer, Y. (2020). The influence of device type on querying behavior and learning outcomes in a searching as learning task with a laptop or smartphone. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR’20) (pp. 373–377). New York: Association for Computing Machinery (ACM).Google Scholar

Deubel, H., & Schneider, W. X. (1996). Saccade target selection and object recognition: Evidence for a common attentional mechanism. Vision Research, 36(12), 1827–1837.Google Scholar

Dewhurst, R., Foulsham, T., Jarodzka, H., Johansson, R., Holmqvist, K., & Nyström, M. (2018). How task demands influence scanpath similarity in a sequential number-search task. Vision Research, 149, 9–23.Google Scholar

Duchowski, A. T. (2003). Eye Tracking Methodology: Theory and Practice. Cham: Springer.Google Scholar

Duchowski, A. T. (2018). Gaze-based interaction: A 30 year retrospective. Computers & Graphics, 73, 59–69.Google Scholar

Eitel, A., Endres, T., & Renkl, A. (2020). Self-management as a bridge between cognitive load and self-regulated learning: The illustrative case of seductive details. Educational Psychology Review, 32(4), 1073–1087.Google Scholar

Eivazi, S., & Bednarik, R. (2011). Predicting problem-solving behavior and performance levels from visual attention data. In Proceedings of 2nd Workshop on Eye Gaze in Intelligent Human Machine Interaction at IUI 2011 (pp. 9–16). New York: ACM.Google Scholar

Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Manual for Kit of Factor-Referenced Cognitive Tests. Princeton, NJ: Educational Testing Service.Google Scholar

Emhardt, S., Wermeskerken, M., Scheiter, K., & Gog, T. (2020). Inferring task performance and confidence from displays of eye movements. Applied Cognitive Psychology, 34(6), 1430–1443.Google Scholar

Ericsson, K. A. (2006). Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (eds.), The Cambridge Handbook of Expertise and Expert Performance (pp. 223–241). Cambridge: Cambridge University Press.Google Scholar

Ericsson, K. A. (2018). Capturing expert thought with protocol analysis: Concurrent verbalisations of thinking during experts’ performance on representative tasks. In Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.), Expertise and Expert Perfomance (pp. 192–212). Cambridge: Cambridge University Press.Google Scholar

Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.) (2018). Expertise and Expert Performance (2nd ed.). Cambridge: Cambridge University Press.Google Scholar

Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal adaption to task constraints. Annual Reviews in Psychology, 47, 273–305.Google Scholar

Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215–251.Google Scholar

Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.Google Scholar

Fiedler, S., Schulte-Mecklenbeck, M., Renkewitz, F., & Orquin, J. L. (2019). Increasing reproducibility of eye-tracking studies. In Schulte-Mecklenbeck, M., Kühberger, A., & Johnson, J. G. (eds.), A Handbook of Process Tracing Methods (pp. 65–75). Abingdon: Routledge.Google Scholar

Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38(4), 281–288.Google Scholar

Fiorella, L., & Mayer, R. E. (2014). Role of expectations and explanations in learning by teaching. Contemporary Educational Psychology, 39(2), 75–85.Google Scholar

Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., & Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Frontiers in Neuroscience, 8, 385.Google Scholar

Gerlic, I., & Jausovec, N. (1999). Multimedia: Differences in cognitive processes observed with EEG. Educational Technology Research and Development, 47(3), 5–14.Google Scholar

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.Google Scholar

Hansen, J. P. (1991). The use of eye mark recordings to support verbal retrospection in software testing. Acta Psychologica, 76(1), 31–49.Google Scholar

Harteis, C., Kok, E., & Jarodzka, H. (2018). New measurements of learning: Emerging chances and challenges of process measures [double Special Issue]. Frontline Learning Research, 6(2–3), 1–249.Google Scholar

Hartmann, C., Gog, T., & Rummel, N. (2020). Do examples of failure effectively prepare students for learning from subsequent instruction? Applied Cognitive Psychology, 34(4), 879–889.Google Scholar

Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations – A meta-analytic review. Educational Psychology Review, 22(3), 245–269.Google Scholar

Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford: Oxford University Press.Google Scholar

Hoogerheide, V., & Roelle, J. (2020). Example-based learning: New theoretical perspectives and use-inspired advances to a contemporary instructional approach. Applied Cognitive Psychology, 34(4), 787–792.Google Scholar

Hoogerheide, V., van Wermeskerken, M., van Nassau, H., & van Gog, T. (2018). Model-observer similarity and task-appropriateness in learning from video modeling examples: Do model and student gender affect test performance, self-efficacy, and perceived competence? Computers in Human Behavior, 89, 457–464.Google Scholar

Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.Google Scholar

Hummel, H. G. K., Nadolski, R. J., Eshuis, J., Slootmaker, A., & Storm, J. (2021). Serious game in introductory psychology for professional awareness: Optimal learner control and authenticity. British Journal of Educational Technology, 52(1), 125–141.Google Scholar

Jaarsma, T., Jarodzka, H., Nap, M., van Merriënboer, J. J. G., & Boshuizen, H. P. A. (2015). Expertise in clinical pathology: Combining the visual and cognitive perspective. Advances in Health Sciences Education, 20(4), 1089–1106.Google Scholar

Jacob, L., Lachner, A., & Scheiter, K. (2020). Learning by explaining orally or in written form? Text complexity matters. Learning and Instruction, 68, 101344.Google Scholar

Jarodzka, H., Balslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2012). Conveying clinical reasoning based on visual observation via eye-movement modelling examples. Instructional Science, 40(5), 813–827.Google Scholar

Jarodzka, H., & Boshuizen, H. P. A. (2017). Unboxing the black box of visual expertise in medicine. Frontline Learning Research, 5(3), 167–183.Google Scholar

Jarodzka, H., Holmqvist, K., & Gruber, H. (2017). Eye tracking in educational sscience: Theoretical frameworks and research agendas. Journal of Eye Movement Research, 10(1), 1–18.Google Scholar

Jarodzka, H., Holmqvist, K., & Nyström, M. (2010). A vector-based, multidimensional scanpath similarity measure. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications - ETRA’10, Austin, TX, March 2010 (pp. 211–218). https://doi.org/10.1145/1743666.1743718 Google Scholar

Jarodzka, H., Janssen, N., Kirschner, P. A., & Erkens, G. (2015). Avoiding split attention in computer-based testing: Is neglecting additional information facilitative?: Avoiding split attention in computer-based testing. British Journal of Educational Technology, 46(4), 803–817.Google Scholar

Jarodzka, H., Scheiter, K., Gerjets, P., & van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20(2), 146–154.Google Scholar

Jarodzka, H., van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2013). Learning to see: Guiding students’ attention via a Model’s eye movements fosters learning. Learning and Instruction, 25, 62–70.Google Scholar

Jiang, D., Kalyuga, S., & Sweller, J. (2018). The curious case of improving foreign language listening skills by reading rather than listening: An expertise reversal effect. Educational Psychology Review, 30(3), 1139–1165.Google Scholar

Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education, 47, 100758.CrossRef Google Scholar

Just, M., & Carpenter, P. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8, 441–480.CrossRef Google Scholar

Kalyuga, S. (2006a). Instructing and Testing Advanced Learners: A Cognitive Load Approach. New York: Nova Science Publishers.Google Scholar

Kalyuga, S. (2006b). Rapid cognitive assessment of learners’ knowledge structures. Learning and Instruction, 16(1), 1–11.Google Scholar

Kalyuga, S. (2006c). Rapid assessment of learners’ proficiency: A cognitive load approach. Educational Psychology, 26(6), 735–749.Google Scholar

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.Google Scholar

Kalyuga, S. (2008). When less is more in cognitive diagnosis: A rapid online method for diagnosing learner task-specific expertise. Journal of Educational Psychology, 100(3), 603–612.Google Scholar

Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–32.Google Scholar

Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.Google Scholar

Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53(3), 83–93.Google Scholar

Kant, J. M., Scheiter, K., & Oschatz, K. (2017). How to sequence video modeling examples and inquiry tasks to foster scientific reasoning. Learning and Instruction, 52, 46–58.Google Scholar

Karpf, D. A. (1973). Thinking Aloud in Human Discrimination Learning [PhD Thesis]. State University of New York.Google Scholar

Kok, E. M., & Jarodzka, H. (2017a). Before your very eyes: The value and limitations of eye tracking in medical education. Medical Education, 51(1), 114–122.Google Scholar

Kok, E. M., & Jarodzka, H. (2017b). Beyond your very eyes: Eye movements are necessary, not sufficient. Medical Education, 51(11), 1190.Google Scholar

Kostons, D., van Gog, T., & Paas, F. (2009). How do I do? Investigating effects of expertise and performance-process records on self-assessment. Applied Cognitive Psychology, 23(9), 1256–1265.Google Scholar

Kruger, J.-L., & Doherty, S. (2016). Measuring cognitive load in the presence of educational video: Towards a multimodal methodology. Australasian Journal of Educational Technology, 32(6), 19–31.Google Scholar

Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., Lee, M.-H., Chiou, G.-L., Liang, J.-C., & Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.Google Scholar

Lee, J. Y., Donkers, J., Jarodzka, H., & van Merriënboer, J. J. G. (2019). How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking. Computers in Human Behavior, 99, 268–277.Google Scholar

Leijten, M., & van Waes, L. (2013). Keystroke logging in writing research: Using inputlog to analyze and visualize writing processes. Written Communication, 30(3), 358–392.Google Scholar

Leppink, J., Paas, F., van der Vleuten, C. P. M., van Gog, T., & van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.Google Scholar

Lindner, M. A. (2020). Representational and decorative pictures in science and mathematics tests: Do they make a difference? Learning and Instruction, 68, 101345.Google Scholar

Litchfield, D., & Ball, L. J. (2011). Rapid communication: Using another’s gaze as an explicit aid to insight problem solving. Quarterly Journal of Experimental Psychology, 64(4), 649–656.Google Scholar

Liu, T., Lin, Y., Hsu, C., Hsu, C., & Paas, F. (2021). Learning from animations and computer simulations: Modality and reverse modality effects. British Journal of Educational Technology, 52(1), 304–317.Google Scholar

Liversedge, S., Gilchrist, I., & Everling, S. (2011). The Oxford Handbook of Eye Movements. Oxford: Oxford University Press.Google Scholar

Mason, L., Pluchino, P., & Tornatora, M. C. (2015). Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning. Contemporary Educational Psychology, 41, 172–187.Google Scholar

Mayer, R. E. (2005). Introduction to multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 1–16). Cambridge: Cambridge University Press.Google Scholar

McIntyre, N. A., Jarodzka, H., & Klassen, R. M. (2019). Capturing teacher priorities: Using real-world eye-tracking to investigate expert teacher priorities across two cultures. Learning and Instruction, 60, 215–224.Google Scholar

McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 1–30.Google Scholar

Menendez, D., Rosengren, K. S., & Alibali, M. W. (2020). Do details bug you? Effects of perceptual richness in learning about biological change. Applied Cognitive Psychology, 34(5), 1101–1117.CrossRef Google Scholar

Merkt, M., Ballmann, A., Felfeli, J., & Schwan, S. (2018). Pauses in educational videos: Testing the transience explanation against the structuring explanation. Computers in Human Behavior, 89, 399–410.Google Scholar

Meyer, D. K., & Schutz, P. A. (2020). Why talk about qualitative and mixed methods in educational psychology? Introduction to special issue. Educational Psychologist, 55(4), 193–196.Google Scholar

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.Google Scholar

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational Psychology Review, 19(3), 309–326.Google Scholar

Nelson, T. O., & Dunlosky, J. (1991). When people’s judgments of learning (JOLs) are extremely accurate at predicting subsequent recall: The “delayed-JOL effect.” Psychological Science, 2(4), 267–271.Google Scholar

Ögren, M., Nyström, M., & Jarodzka, H. (2017). There’s more to the multimedia effect than meets the eye: Is seeing pictures believing? Instructional Science, 45(2), 263–287.Google Scholar

Oliva, M., Niehorster, D. C., Jarodzka, H., & Holmqvist, K. (2017). Influence of coactors on saccadic and manual responses. I-Perception, 8(1), 1–23.Google Scholar

Paas, F. (1992). Training strategies for attaining transfer of problem-solving skills in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434.Google Scholar

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.Google Scholar

Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.Google Scholar

Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological Review, 76(3), 241–263.Google Scholar

Park, B., Korbach, A., & Brünken, R. (2020). Does thinking-aloud affect learning, visual information processing and cognitive load when learning with seductive details as expected from self-regulation perspective? Computers in Human Behavior, 111, 106411.Google Scholar

Peters, M., Laeng, B., Jackson, M., Zaiyouna, R., & Richardson, C. (1995). A redrawn Vandenberg and Kuse mental rotations test: Different versions and factors that affect performance. Brain and Cognition, 28, 39–58.Google Scholar

Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.Google Scholar

Rayner, K. (2009). The 35th Sir Frederick Bartlett lecture: Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62(8), 1457–1506.Google Scholar

Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12, 529–556.Google Scholar

Renkl, A., & Atkinson, R. K. (2002). Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive Learning Environments, 10(2), 105–119.Google Scholar

Rey, G. D., & Fischer, A. (2013). The expertise reversal effect concerning instructional explanations. Instructional Science, 41(2), 407–429.Google Scholar

Richter, J., & Scheiter, K. (2019). Studying the expertise reversal of the multimedia signaling effect at a process level: Evidence from eye tracking. Instructional Science, 47(6), 627–658.Google Scholar

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.Google Scholar

Salmerón, L., Delgado, P., & Mason, L. (2020). Using eye‐movement modelling examples to improve critical reading of multiple webpages on a conflicting topic. Journal of Computer Assisted Learning, 36(6), 1038–1051.Google Scholar

Salmerón, L., Gil, L., Bråten, I., & Strømsø, H. (2010). Comprehension effects of signalling relationships between documents in search engines. Computers in Human Behavior, 26(3), 419–426.Google Scholar

Saß, S., Schütte, K., & Lindner, M. A. (2017). Test-takers’ eye movements: Effects of integration aids and types of graphical representations. Computers & Education, 109, 85–97.Google Scholar

Scarapicchia, V., Brown, C., Mayo, C., & Gawryluk, J. R. (2017). Functional magnetic resonance imaging and functional near-infrared spectroscopy: Insights from combined recording studies. Frontiers in Human Neuroscience, 11, 419.Google Scholar

Scharinger, C. (2018). Fixation-related EEG frequency band power analysis. Frontline Learning Research, 6(3), 57–71.Google Scholar

Scheiter, K., Ackerman, R., & Hoogerheide, V. (2020). Looking at mental effort appraisals through a metacognitive lens: Are they biased? Educational Psychology Review, 32, 1003–1027.Google Scholar

Scheiter, K., Brucker, B., & Ainsworth, S. (2020). “Now move like that fish”: Can enactment help learners come to understand dynamic motion presented in photographs and videos? Computers & Education, 155, 103934.Google Scholar

Schmeck, A., Opfermann, M., van Gog, T., Paas, F., & Leutner, D. (2015). Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings. Instructional Science, 43(1), 93–114.Google Scholar

Schneider, S., Beege, M., Nebel, S., & Rey, G. D. (2018). A meta-analysis of how signaling affects learning with media. Educational Research Review, 23, 1–24.Google Scholar

Schneider, S., Nebel, S., Beege, M., & Rey, G. D. (2018). The autonomy-enhancing effects of choice on cognitive load, motivation and learning with digital media. Learning and Instruction, 58, 161–172.Google Scholar

Schweizer, K., & DiStefano, C. (2016). Principles and Methods of Test Construction: Standards and Recent Advances (Vol. 3). Toronto: Hogrefe.Google Scholar

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.Google Scholar

Skuballa, I. T., Xu, K. M., & Jarodzka, H. (2019). The impact of co-actors on cognitive load: When the mere presence of others makes learning more difficult. Computers in Human Behavior, 101, 30–41.Google Scholar

Strijbos, J.-W., Martens, R. L., Prins, F. J., & Jochems, W. M. G. (2006). Content analysis: What are they talking about? Computers & Education, 46(1), 29–48.Google Scholar

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292.Google Scholar

Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics. Applied Cognitive Psychology, 34(4), 890–905.Google Scholar

Touvinen, J. E., & Paas, F. (2004). Exploring multidimesional approaches to the efficiency of instructional conditions. Instructional Science, 32, 133–152.Google Scholar

Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020). Learning analytics in European higher education – Trends and barriers. Computers & Education, 155, 103933.Google Scholar

van der Meij, H., Rensink, I., & van der Meij, J. (2018). Effects of practice with videos for software training. Computers in Human Behavior, 89, 439–445.Google Scholar

van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25(3), 785–791.Google Scholar

van Gog, T., & Paas, F. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational Psychologist, 43(1), 16–26.Google Scholar

van Gog, T., Paas, F., van Merriënboer, J. J. G., & Witte, P. (2005). Uncovering expertise-related differences in troubleshooting performance. Combining eye movement and concurrent verbal protocol data. Applied Cognitive Psychology, 19, 237–244.Google Scholar

van Laer, S., & Elen, J. (2019). The effect of cues for calibration on learners’ self-regulated learning through changes in learners’ learning behaviour and outcomes. Computers & Education, 135, 30–48.Google Scholar

van Marlen, T., van Wermeskerken, M., Jarodzka, H., & van Gog, T. (2018). Effectiveness of eye movement modeling examples in problem solving: The role of verbal ambiguity and prior knowledge. Learning and Instruction, 58, 274–283.Google Scholar

van Meeuwen, L. W., Jarodzka, H., Brand-Gruwel, S., Kirschner, P. A., de Bock, J. J. P. R., & van Merriënboer, J. J. G. (2014). Identification of effective visual problem solving strategies in a complex visual domain. Learning and Instruction, 32, 10–21.Google Scholar

van Orden, K. F., Limbert, W., Makeig, S., & Jung, T.-P. (2001). Eye activity correlates of workload during a visuospatial memory task. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(1), 111–121.Google Scholar

van Wermeskerken, M., Ravensbergen, S., & van Gog, T. (2018). Effects of instructor presence in video modeling examples on attention and learning. Computers in Human Behavior, 89, 430–438.Google Scholar

Wang, F., Zhao, T., Mayer, R. E., & Wang, Y. (2020). Guiding the learner’s cognitive processing of a narrated animation. Learning and Instruction, 69, 101357.Google Scholar

Wells, A., Parong, J., & Mayer, R. E. (2020). Limits on training inhibitory control with a focused video game. Journal of Cognitive Enhancement, 5(1), 785–797.Google Scholar

Wolff, C. E., Jarodzka, H., & Boshuizen, H. P. A. (2017). See and tell: Differences between expert and novice teachers’ interpretations of problematic classroom management events. Teaching and Teacher Education, 66, 295–308.Google Scholar

Wolff, C. E., Jarodzka, H., van den Bogert, N., & Boshuizen, H. P. A. (2016). Teacher vision: Expert and novice teachers’ perception of problematic classroom management scenes. Instructional Science, 44(3), 243–265.Google Scholar

Wong, M., Castro-Alonso, J. C., Ayres, P., & Paas, F. (2018). Investigating gender and spatial measurements in instructional animation research. Computers in Human Behavior, 89, 446–456.Google Scholar

Xu, K. M., Koorn, P., de Koning, B. B., Skuballa, I. T., Lin, L., Hendrikx, M., Marsh, H. W., Sweller, J., & Paas, F. (2020). A growth mindset lowers perceived cognitive load and improves learning: Integrating motivation to cognitive load. Journal of Educational Psychology, Advance online publication. https://doi.org/10.1037/edu0000631 Google Scholar

Book contents

Part I - Background

Summary

Access options

References

References

References

References

References

Save book to Kindle

Save book to Dropbox

Save book to Google Drive