Book contents
- The Cambridge Handbook of Multimedia Learning
- The Cambridge Handbook of Multimedia Learning
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Preface
- Acknowledgments
- Part I Background
- Part II Theoretical Foundations
- Part III Basic Principles of Multimedia Learning
- Part IV Principles for Reducing Extraneous Processing in Multimedia Learning
- Part V Principles for Managing Essential Processing in Multimedia Learning
- Part VI Principles Based on Social and Affective Features of Multimedia Learning
- Part VII Principles Based on Generative Activity in Multimedia Learning
- Part VIII Multimedia Learning with Media
- Author Index
- Subject Index
- References
Part I - Background
Published online by Cambridge University Press: 19 November 2021
Book contents
- The Cambridge Handbook of Multimedia Learning
- The Cambridge Handbook of Multimedia Learning
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Preface
- Acknowledgments
- Part I Background
- Part II Theoretical Foundations
- Part III Basic Principles of Multimedia Learning
- Part IV Principles for Reducing Extraneous Processing in Multimedia Learning
- Part V Principles for Managing Essential Processing in Multimedia Learning
- Part VI Principles Based on Social and Affective Features of Multimedia Learning
- Part VII Principles Based on Generative Activity in Multimedia Learning
- Part VIII Multimedia Learning with Media
- Author Index
- Subject Index
- References
Summary
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- Chapter
- Information
- The Cambridge Handbook of Multimedia Learning , pp. 1 - 54Publisher: Cambridge University PressPrint publication year: 2021
References
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.CrossRefGoogle 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
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.CrossRefGoogle Scholar
Mayer, R. E. (2019). Computer games in education. Annual Review of Psychology, 70, 531–549.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.CrossRefGoogle 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. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4, 417–423.CrossRefGoogle ScholarPubMed
Baddeley, A. (2012). Working memory, theories models and controversy. The Annual Review of Psychology, 63, 12.11–12.29.CrossRefGoogle Scholar
Baddeley, A., & Andrade, J. (2000). Working memory and the vividness of imagery. Journal of Experimental Psychology: General, 129, 126–145.CrossRefGoogle ScholarPubMed
Baddeley, A. D., Eysenck, M. W., & Anderson, M. C. (2020). Memory. New York: Psychology Press.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23, 1–19.CrossRefGoogle 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.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40(1), 1–17.CrossRefGoogle 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
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Mayer, R. E. (2019). Computer games in education. Annual Review of Psychology, 70, 531–549.CrossRefGoogle Scholar
Mayer, R. E. (2020). Multimedia Learning (3rd ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287.CrossRefGoogle 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.CrossRefGoogle Scholar
Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171, 701–703.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Skulmowski, A., & Rey, G. D. (2017b). Measuring cognitive load in embodied learning settings. Frontiers in Psychology, 8, 1191.CrossRefGoogle ScholarPubMed
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: SpringerCrossRefGoogle Scholar
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Ackerman, P. L. (2003). Cognitive ability and non-ability trait determinants of expertise. Educational Researcher, 32(8), 15–20.CrossRefGoogle Scholar
Aksayli, N., Sala, G., & Gobet, F. (2019). The cognitive and academic benefits of CogMed: A meta-analysis. Educational Research Review, 27, 229–243.CrossRefGoogle Scholar
Allcoat, D., & von Mühlenen, A. (2018). Learning in virtual reality: Effects on performance, emotion and engagement. Research in Learning Technology, 26, 1–13.CrossRefGoogle Scholar
Alpert, W. T., Couch, K. A., & Harmon, O. R. (2016). A randomized assessment of online learning. American Economic Review, 106(5), 378–382.CrossRefGoogle Scholar
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4), 369–406.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Calude, C., & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science, 22, 595–612.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Clark, R. E. (1982). Antagonism between achievement and enjoyment in ATI studies. Educational Psychologist, 17(2), 92–101.CrossRefGoogle Scholar
Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.CrossRefGoogle 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 ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Gredler, M., & Shields, C. (2004). Does no one read Vygotsky’s words? Commentary on Glassman. Educational Researcher, 33(2), 21–25.CrossRefGoogle 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.CrossRefGoogle Scholar
Heeter, C. (1992). Being there: The subjective experience of presence. Presence: Teleoperators and Virtual Environments, 1(2), 262–271.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
James, W. B., & Gardner, D. L. (1995). Learning styles: Implications for distance learning. New Directions for Adult and Continuing Education, 67, 19–31.CrossRefGoogle Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle Scholar
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171.CrossRefGoogle 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.CrossRefGoogle Scholar
Koedinger, K. R., & Aleven, V. (2007). The assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19, 239–264.CrossRefGoogle Scholar
Kozma, R. (1994) Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42(2), 7–19.CrossRefGoogle Scholar
Kramer, N. C., & Bente, G. (2010) Personalizing e-learning: The social effects of pedagogical agents. Educational Psychology Review, 22(1), 71–87.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Lilienfeld, S. (2017). Psychology’s replication crisis and the grant culture: Righting the ship. Perspectives on Psychological Science, 12, 660–664.CrossRefGoogle Scholar
Lohman, D. F. (1986). Predicting mathemathantic effects in the teaching of higher-order thinking skills, Educational Psychologist, 21(3), 191–208.CrossRefGoogle Scholar
Ma, J., & Nickerson, J. V. (2006). Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Computing Surveys, 38(3), 1–14.CrossRefGoogle 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.CrossRefGoogle Scholar
Mayer, R. (2001). What good is educational psychology? The case of cognition and instruction. Educational Psychologist, 36(2), 83–88.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Redick, T. (2015). Working memory training and interpreting interactions in intelligence interventions. Intelligence, 50, 14–20.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32, 77–112.CrossRefGoogle 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.CrossRefGoogle Scholar
Sala, G., & Gobet, F. (2020). Working memory training in typically developing children: A multilevel meta-analysis. Psychonomic Bulletin & Review, 27, 423–434.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Schroeder, N. L., & Gotch, C. M. (2015). Persisting issues in pedagogical agent research. Journal of Educational Computing Research, 53(2), 183–204.CrossRefGoogle 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.CrossRefGoogle Scholar
Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of Human–Computer Studies, 74, 14–31.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Sternberg, R. J., Grigorenko, E. L., & Kidd, K. K. (2005). Intelligence, race, and genetics. American Psychologist, 60(1), 46–59.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle Scholar
Sweller, J. (2008). Instructional implications of David C. Geary’s evolutionary educational psychology, Educational Psychologist, 43(4), 214–216.CrossRefGoogle Scholar
Tobias, S., & Duffy, T. M. (eds.) (2009). Constructivist Instruction: Success or Failure. New York: Routledge.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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
VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Wang, Y. (2016). Big opportunities and big concerns of big data in education. TechTrends, 60, 381–384.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
References
Ahern, S., & Beatty, J. (1979). Pupillary responses during information processing vary with scholastic aptitude test scores. Science, 205(4412), 1289–1292.CrossRefGoogle ScholarPubMed
Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389–400.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1–29.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, 411–417.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor-Analytic Studies. Cambridge: Cambridge University Press.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Daneman, M., & Carpenter, P. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466.CrossRefGoogle Scholar
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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).CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Duchowski, A. T. (2003). Eye Tracking Methodology: Theory and Practice. Cham: Springer.CrossRefGoogle Scholar
Duchowski, A. T. (2018). Gaze-based interaction: A 30 year retrospective. Computers & Graphics, 73, 59–69.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.) (2018). Expertise and Expert Performance (2nd ed.). Cambridge: Cambridge University Press.CrossRefGoogle 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.CrossRefGoogle Scholar
Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215–251.CrossRefGoogle Scholar
Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.CrossRefGoogle 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.CrossRefGoogle Scholar
Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38(4), 281–288.CrossRefGoogle Scholar
Fiorella, L., & Mayer, R. E. (2014). Role of expectations and explanations in learning by teaching. Contemporary Educational Psychology, 39(2), 75–85.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations – A meta-analytic review. Educational Psychology Review, 22(3), 245–269.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Jacob, L., Lachner, A., & Scheiter, K. (2020). Learning by explaining orally or in written form? Text complexity matters. Learning and Instruction, 68, 101344.CrossRefGoogle 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.CrossRefGoogle Scholar
Jarodzka, H., & Boshuizen, H. P. A. (2017). Unboxing the black box of visual expertise in medicine. Frontline Learning Research, 5(3), 167–183.CrossRefGoogle 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.CrossRefGoogle 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/10.1145/1743666.1743718CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Just, M., & Carpenter, P. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8, 441–480.CrossRefGoogle 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.CrossRefGoogle Scholar
Kalyuga, S. (2006c). Rapid assessment of learners’ proficiency: A cognitive load approach. Educational Psychology, 26(6), 735–749.CrossRefGoogle Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.CrossRefGoogle 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.CrossRefGoogle Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–32.CrossRefGoogle Scholar
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Kok, E. M., & Jarodzka, H. (2017b). Beyond your very eyes: Eye movements are necessary, not sufficient. Medical Education, 51(11), 1190.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Lindner, M. A. (2020). Representational and decorative pictures in science and mathematics tests: Do they make a difference? Learning and Instruction, 68, 101345.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Liversedge, S., Gilchrist, I., & Everling, S. (2011). The Oxford Handbook of Eye Movements. Oxford: Oxford University Press.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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(2), 174–179.CrossRefGoogle 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.CrossRefGoogle Scholar
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 1–30.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.CrossRefGoogle 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.CrossRefGoogle Scholar
Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological Review, 76(3), 241–263.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12, 529–556.CrossRefGoogle 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.CrossRefGoogle Scholar
Rey, G. D., & Fischer, A. (2013). The expertise reversal effect concerning instructional explanations. Instructional Science, 41(2), 407–429.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Scharinger, C. (2018). Fixation-related EEG frequency band power analysis. Frontline Learning Research, 6(3), 57–71.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Schweizer, K., & DiStefano, C. (2016). Principles and Methods of Test Construction: Standards and Recent Advances (Vol. 3). Toronto: Hogrefe.CrossRefGoogle Scholar
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Touvinen, J. E., & Paas, F. (2004). Exploring multidimesional approaches to the efficiency of instructional conditions. Instructional Science, 32, 133–152.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
van Gog, T., & Paas, F. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational Psychologist, 43(1), 16–26.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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/edu0000631CrossRefGoogle Scholar