Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147–179.Google Scholar

Aleven, V., & Koedinger, K. R. (2013). Knowledge component approaches to learner modeling. In Sottilare, R., Graesser, A., Hu, X., & Holden, H. (eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems (Learner Modeling, 1, pp. 165–182). Orlando, FL: US Army Research Laboratory.Google Scholar

Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2016). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 26(1), 205–223.Google Scholar

Aleven, V., McLaren, B. M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., Ringenberg, M., & Koedinger, K. R. (2016). Example-tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, 26(1), 224–269.Google Scholar

Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2017). Instruction based on adaptive learning technologies. In Mayer, R. E., & Alexander, P. (eds.), Handbook of Research on Learning and Instruction (2nd ed., pp. 522–560). New York: Routledge.Google Scholar

Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP tutor. Cognitive Science, 13(4), 467–505.Google Scholar

Anderson, J. R., Corbett, A. T., Koedinger, K., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of Learning Sciences, 4, 167–207.Google Scholar

Baker, R. S. J. D., Corbett, A. T., & Koedinger, K. R. (2007). The difficulty factors approach to the design of lessons in intelligent tutor curricula. International Journal of Artificial Intelligence in Education, 17(4), 341–369.Google Scholar

Butcher, K., & Aleven, V. (2013). Using student interactions to foster rule-diagram mapping during problem solving in an intelligent tutoring system. Journal of Educational Psychology, 105(4), 988–1009.CrossRefGoogle Scholar

Carvalho, P. F., & Goldstone, R. L. (2014). Putting category learning in order: Category structure and temporal arrangement affect the benefit of interleaved over blocked study. Memory & Cognition, 42, 481–495.CrossRefGoogle ScholarPubMed

Carvalho, P. F., & Goldstone, R. L. (2019). When does interleaving practice improve learning?. In Dunlosky, J., & Rawson, K. (eds.), The Cambridge Handbook of Cognition and Education (pp. 411–436). Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.Google Scholar

Clark, R. E., Feldon, D., van Merriënboer, J., Yates, K., & Early, S. (2007). Cognitive task analysis. In Spector, J. M., Merrill, M. D., van Merrie¨nboer, J. J. G., & Driscoll, M. P. (eds.), Handbook of Research on Educational Communications and Technology (3rd ed., pp. 577–593). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278.CrossRefGoogle Scholar

Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In CHI ‘01: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 245–252). New York: ACM Press.Google Scholar

Corbett, A. T., McLaughlin, M., & Scarpinatto, K. C. (2000). Modeling student knowledge: Cognitive Tutors in high school and college. User Modeling and User-Adapted Interaction, 10, 81–108.Google Scholar

Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257.CrossRefGoogle ScholarPubMed

Deslauriers, L., Schelew, E., & Wieman, C. (2011). Improved learning in a large-enrollment physics class. Science, 332(603), 862–864.CrossRefGoogle Scholar

Doroudi, S., Holstein, K., Aleven, V., & Brunskill, E. (2015). Towards understanding how to leverage sense-making, induction/refinement and fluency to improve robust learning. In Santos, O. C., Boticario, J. G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., & Desmarais, M. (eds.), Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015 (pp. 376–379). Worcester, MA: International Educational Data Mining Society.Google Scholar

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.Google Scholar

Ericsson, K. A. & Simon, H. A. (1985). Protocol analysis. In van Dijk, T. A. (ed.), Handbook of Discourse Analysis (Vol. 2; pp. 259–268). London: Academic Press.Google Scholar

Fries, L., Son, J. Y., Givvin, K. B. & Stigler, J. W. (2020). Practicing connections: A framework to guide instructional design for developing understanding in complex domains. Educational Psychology Review, 33, 739–762.Google Scholar

Hattie, J. (2008). Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. New York: Routledge.Google Scholar

Holstein, K., Aleven, V., & Rummel, N. (2020). A conceptual framework for human–AI hybrid adaptivity in education. In Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., & Millán, E. (eds.), Proceedings, 21th International Conference on Artificial Intelligence in Education, AIED 2020 (pp. 240–254). Cham: Springer.Google Scholar

Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In Rosé, C. P., Martínez-Maldonado, R., Hoppe, H. U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., & du Boulay, B. (eds.), Proceedings, 19th International Conference on Artificial Intelligence in Education, AIED 2018 (Part 1, pp. 154–168). Cham: Springer.Google Scholar

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52.Google Scholar

Huang, Y., Aleven, V., McLaughlin, E., & Koedinger, K. (2020). A general multi-method approach to design-loop adaptivity in intelligent tutoring systems. Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II, 12164, 124–129.Google Scholar

Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588.CrossRefGoogle Scholar

Kellogg, R. T., & Whiteford, A. P. (2009). Training advanced writing skills: The case for deliberate practice. Educational Psychologist, 44, 250–266.Google Scholar

Kellman, P. J., & Krasne, S. (2018). Accelerating expertise: Perceptual and adaptive learning technology in medical learning. Medical Teacher, 40(8), 797–802.Google Scholar

Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. Invited paper. In Mewborn, D., Sztajn, P., White, D. Y., Wiegel, H. G., Bryant, R. L., & Nooney, K. (eds.), Proceedings of the 24th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 21–49). Columbus, OH: ERIC Clearinghouse for Science, Mathematics, and Environmental Education.Google Scholar

Koedinger, K. R. & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239–264.Google Scholar

Koedinger, K. R., & Aleven, V. (2016). An interview reflection on “intelligent tutoring goes to school in the big city.” International Journal of Artificial Intelligence in Education, 26(1), 13–24.Google Scholar

Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8(1), 30–43.Google Scholar

Koedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science, 342, 935–937.Google Scholar

Koedinger, K. R., & Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning sciences to the classroom. In Sawyer, R. K. (ed.), The Cambridge Handbook of the Learning Sciences (pp. 61–78). New York: Cambridge University Press.Google Scholar

Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798.Google Scholar

Koedinger, K. R. & McLaughlin, E. A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In Ohlsson, S., & Catrambone, R. (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 471–476). Austin, TX: Cognitive Science Society.Google Scholar

Koedinger, K. R., & McLaughlin, E. A. (2016). Closing the loop with quantitative cognitive task analysis. In Barnes, T., Chi, M., and Feng, M. (eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 412–417). Raleigh, NC: International Conference on Educational Data Mining (EDM).Google Scholar

Koedinger, K. R., McLaughlin, E. A., & Stamper, J. C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 17–24). Greece: Chania.Google Scholar

Li, N., Cohen, W. W., & Koedinger, K. R. (2013). Problem order implications for learning. International Journal of Artificial Intelligence in Education, 23(1–4), 71–93.Google Scholar

Li, N., Matsuda, N., Cohen, W. W., and Koedinger, K. R. (2015). Integrating representation learning and skill learning in a human-like intelligent agent. Artificial Intelligence, 219, 67–91.Google Scholar

Liu, R., & Koedinger, K. R. (2017). Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning gains. Journal of Educational Data Mining, 9(1), 25–41.Google Scholar

Liu, R., Koedinger, K. R., & McLaughlin, E. A. (2014). Interpreting model discovery and testing generalization to a new dataset. In Stamper, J., Pardos, Z., Mavrikis, M., & McLaren, B. M. (eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 107–113). Worcester, MA: International Conference on Educational Data Mining.Google Scholar

Lovett, M. C. (1998). Cognitive task analysis in service of intelligent tutoring system design: A case study in statistics. In International Conference on Intelligent Tutoring Systems (pp. 234–243). Berlin: Springer.Google Scholar

Lovett, M. C., Meyer, O., & Thille, C. (2008). JIME-The open learning initiative: Measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education, 2008(1), Art. 13.Google Scholar

MacLellan, C. J. (2017). Computational Models of Human Learning: Applications for Tutor Development, Behavior Prediction, and Theory Testing [Doctoral Dissertation]. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA.Google Scholar

MacLellan, C. J., Harpstead, E., Patel, R., & Koedinger, K. (2016). The apprentice learner architecture: Closing the loop between learning theory and educational data. In Proceedings of the 9th International Conference in Educational Data Mining (pp. 151–158). Worcester, MA: International Educational Data Mining Society.Google Scholar

Martin, B., Mitrovic, T., Mathan, S., & Koedinger, K.R. (2011). Evaluating and improving adaptive educational systems with learning curves. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 21(3), 249–283.Google Scholar

Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.Google Scholar

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

McDaniel, M. A., Agarwal, P. K., Huelser, B. J., McDermott, K. B., & Roediger, H. L. III (2011). Test-enhanced learning in a middle school science classroom: The effects of quiz frequency and placement. Journal of Educational Psychology, 103(2), 399.Google Scholar

Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, 60(1), 264–272.Google Scholar

Moreno, R. (2004). Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery‐based multimedia. Instructional Science, 32, 99–113.CrossRefGoogle Scholar

Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent‐based multimedia game. Journal of Educational Psychology, 97, 117–128.Google Scholar

Nagashima, T., Bartel, A. N., Silla, E., Vest, N., Alibali, M. W., & Aleven, V. (2020). Enhancing conceptual knowledge in early algebra through scaffolding diagrammatic self-explanation. In Proceedings of International Conference of the Learning Sciences, ICLS 2020 (Part 1, pp. 35–42). Nashville, TN: International Society of the Learning Sciences.Google Scholar

Ohlsson, S. (1994). Constraint based student modeling. In Greer, J. E., & McCalla, G. (eds.), Student Modelling: The Key to Individualized Knowledge-Based Instruction. NATO ASI Series (Series F: Computer and Systems Sciences) (vol. 125, pp. 167–189). Berlin: Springer.CrossRefGoogle Scholar

Paas, F. G. W. C., & van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122–133.Google Scholar

Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2014). Effectiveness of cognitive tutor algebra I at scale. Educational Evaluation and Policy Analysis, 36(2), 127–144.CrossRefGoogle Scholar

Patel, R. (2017). Addressing Interference in Fraction Learning: What Difficulties are Desirable? [Doctoral Dissertation]. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA.Google Scholar

Patel, R., Liu, R., & Koedinger, K. (2016). When to block versus interleave practice? Evidence against teaching fraction addition before fraction multiplication. In Papafragou, A., Grodner, D., Mirman, D., & Trueswell, J. C. (eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2069–2074). Austin, TX: Cognitive Science Society.Google Scholar

Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717–761.Google Scholar

Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30–46.Google Scholar

Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249–255.CrossRefGoogle ScholarPubMed

Roediger, H. L., and Karpicke, J. D. (2006a). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17, 249–255.Google Scholar

Roediger, H. L., and Karpicke, J. D. (2006b). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1, 181–210.CrossRefGoogle ScholarPubMed

Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24, 355–367.CrossRefGoogle Scholar

Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics practice problems boosts learning. Instructional Science, 35, 481–498.Google Scholar

Roll, I., Baker, R. S. J. D., Aleven, V., & Koedinger, K. R. (2014) On the benefits of seeking (and avoiding) help in online problem- solving environments. Journal of the Learning Sciences, 23(4), 537–560.Google Scholar

Schnackenberg, H. L., Sullivan, H. J., Leader, L. F., & Jones, E. E. K. (1998). Learner preferences and achievement under differing amounts of learner practice. Educational Technology Research and Development, 46, 5–16.Google Scholar

Schofield, J. W. (1995). Computers and Classroom Culture. New York: Cambridge University Press.Google Scholar

Schooler, L. J., & Anderson, J. R. (1990). The disruptive potential of immediate feedback. In Piattelli-Palmarini, M. (ed.), Proceedings of the Twelfth Annual Conference of the Cognitive Science Society (pp. 702–708). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar

Shih, B., Koedinger, K. R., & Scheines, R. (2008). A response time model for bottom-out hints as worked examples. In Proceedings of the First International Conference on Educational Data Mining, 2008, Montreal, QC, pp. 117–126.Google Scholar

Tofel-Grehl, C., & Feldon, D. F. (2013). Cognitive task analysis-based training: A meta-analysis of studies. Journal of Cognitive Engineering and Decision Making, 7(2), 293–304.Google Scholar

van der Kleij, F. M., Feskens, C. W. R., & Eggen, T. J. H. M. (2015). Effects of feedback in a computer‐based learning environment on students’ learning outcomes: A meta‐analysis. Review of Educational Research, 85(4), 475–511.Google Scholar

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.Google Scholar

VanLehn, K. (2016). Regulative loops, step loops and task loops. International Journal of Artificial Intelligence in Education, 26(1), 107–112.CrossRefGoogle Scholar

Weitekamp, D., Harpstead, E., & Koedinger, K. R. (2020). An interaction design for machine teaching to develop AI tutors. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, April 2020, Honolulu, HI, pp. 1–11.Google Scholar

Wylie, R., Sheng, M., Mitamura, T., & Koedinger, K. R. (2011). Effects of adaptive prompted self-explanation on robust learning of second language grammar. In International Conference on Artificial Intelligence in Education (pp. 588–590). Berlin: Springer.Google Scholar

Yannier, N., Hudson, S. E., & Koedinger, K. R. (2020). Active learning is about more than hands-on: A mixed-reality AI system to support STEM education. International Journal of Artificial Intelligence in Education, 30(1), 74–96.Google Scholar

Atkinson, R. K. (2002). Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology, 94(2), 416–427.Google Scholar

Atkinson, R. K., Mayer, R. E., & Merrill, M. M. (2005). Fostering social agency in multimedia learning: Examining the impact of an animated agent’s voice. Contemporary Educational Psychology, 30(1), 117–139.Google Scholar

Azevedo, R., Landis, R. S., Feyzi-Behnagh, R., Duffy, M., Trevors, G., Harley, J., Bouchet, F., Burlison, J., Taub, M., & Pacampara, N., Yeasin, M., Rahman, A. K. M. M., Tanveer, M. I., & Hossain, G. (2012). The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with MetaTutor. In Cerri, S. A., Clancey, W. J., Papadourakis, G., & Panourgia, K. (eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 212–221). Amsterdam: Springer.Google Scholar

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.Google Scholar

Baylor, A. (2002). Expanding preservice teachers’ metacognitive awareness of instructional planning through pedagogical agents. Educational Technology Research and Development, 50, 5–22.Google Scholar

Baylor, A. L., & Kim, S. (2009). Designing nonverbal communication for pedagogical agents: When less is more. Computers in Human Behavior, 25(2), 450–457.Google Scholar

Beege, M., Schneider, S., Nebel, S., & Rey, G. D. (2020). Does the effect of enthusiasm in a pedagogical agent’s voice depend on mental load in the learner’s working memory? Computers in Human Behavior, 112, 106483.Google Scholar

Brucker, B., Ehlis, A. C., Häußinger, F., Fallgatter, A., & 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

Carlotto, T., & Jaques, P. A. (2016). The effects of animated pedagogical agents in an English-as-a-foreign-language learning environment. International Journal of Human–Computer Studies, 95, 15–26.Google Scholar

Choi, S., & Clark, R. E. (2006). Cognitive and affective benefits of an animated pedagogical agent for learning English as a second language. Journal of Educational Computing Research, 34(4), 441–466.Google Scholar

Cohen, J. A. (1992). A Power primer. Psychological Bulletin, 112, 155–159.Google Scholar

Craig, S. D., Gholson, B., & Driscoll, D. M. (2002). Animated pedagogical agents in multimedia educational environments: Effects of agent properties, picture features and redundancy. Journal of Educational Psychology, 94(2), 428–434.Google Scholar

Craig, S. D., Twyford, J., Irigoyen, N., & Zipp, S. A. (2015). A test of spatial contiguity for virtual human’s gestures in multimedia learning environments. Journal of Educational Computing Research, 53(1), 3–14.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

Dehn, D., & van Mulken, S. (2000). The impact of animated interface agents: A review of empirical research. International Journal of Human–Computer Studies, 52, 1–22.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.Google Scholar

Dunsworth, Q., & Atkinson, R. K. (2007). Fostering multimedia learning of science: Exploring the role of an animated agent’s image. Computers & Education, 49(3), 677–690.Google Scholar

Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463.Google Scholar

Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ Clinical Research, 315, 629–634.Google Scholar

Fiorella, L., & Mayer, R. E. (2016). Effects of observing the instructor draw diagrams on learning from multimedia messages. Journal of Educational Psychology, 108(4), 528–546.Google Scholar

Fiorella, L., & Mayer, R. E. (2018). What works and doesn’t work with instructional video. Computers in Human Behavior, 89, 465–470.Google Scholar

Fiorella, L., Stull, A. T., Kuhlmann, S., & Mayer, R. E. (2019). Instructor presence in video lectures: The role of dynamic drawings, eye contact, and instructor visibility. Journal of Educational Psychology, 111(7), 1162–1171.CrossRefGoogle Scholar

Fiorella, L., van Gog, T., Hoogerheide, V., & Mayer, R. E. (2017). It’s all a matter of perspective: Viewing first-person video modeling examples promotes learning of an assembly task. Journal of Educational Psychology, 109(5), 653–665.CrossRefGoogle Scholar

Frechette, C., & Moreno, R. (2010). The roles of animated pedagogical agents’ presence and nonverbal communication in multimedia learning environments. Journal of Media Psychology: Theories, Methods, and Applications, 22, 61–72.Google Scholar

Graesser, A., Jackson, G. T., Ventura, M., Mueller, J., & Hu, X. (2003). The impact of conversational navigational guides on the learning, use, and perceptions of users of a web site. Paper presented at the meeting of the AAAI Spring Symposium on Agent-Mediated Knowledge Management, March 2003, Stanford, CA.Google Scholar

Hedges, L. V., & Olkin, I. (1985). Statistical Methods for Meta-Analysis. New York: Academic Press.Google Scholar

Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3, 486–504.Google Scholar

Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6(1), 27–54.Google Scholar

Hong, Z. W., Chen, Y. L., & Lan, C. H. (2014). A courseware to script animated pedagogical agents in instructional material for elementary students in English education. Computer Assisted Language Learning, 27(5), 379–394.Google Scholar

Johnson, A. M., Ozogul, G., Moreno, R., & Reisslein, M. (2013). Pedagogical agent signaling of multiple visual engineering representations: The case of the young female agent. Journal of Engineering Education, 102(2), 319–337.Google Scholar

Johnson, A. M., Ozogul, G., & Reisslein, M. (2015). Supporting multimedia learning with visual signalling and animated pedagogical agent: Moderating effects of prior knowledge. Journal of Computer Assisted Learning, 31(2), 97–115.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., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.CrossRefGoogle Scholar

Kang, S. (2014). The Effect of Animated Pedagogical Agent in Multimedia: The Role of Learner Characteristic and Learning Material [Master’s thesis]. Central China Normal University.Google Scholar

Kim, Y., Baylor, A. L., & Shen, E. (2007). Pedagogical agents as learning companions: The impact of agent emotion and gender. Journal of Computer Assisted Learning, 23(3), 220–234.Google Scholar

Kizilkaya, G., & Askar, P. (2008). The effect of an embedded pedagogical agent on the students’ science achievement. Interactive Technology and Smart Education, 5(4), 208–216.Google Scholar

Li, W., Tong, Y., Wang, F., Kang, S., Liu, H., & Yang, C. (2016). Effect of animation pedagogical agent in multimedia learning: The role of learner’s experience and agent preference. Psychological Development and Education, 32(4), 453–462.Google Scholar

Li, W., Wang, F., Mayer, R. E., & Liu, H. (2019). Getting the point: Which kinds of gestures by pedagogical agents improve multimedia learning? Journal of Educational Psychology, 111(8), 1382–1395.Google Scholar

Lin, L., Atkinson, R. K., Christopherson, R. M., Joseph, S. S., & Harrison, C. J. (2013). Animated agents and learning: Does the type of verbal feedback they provide matter? Computers & Education, 67, 239–249.Google Scholar

Lin, L., Ginns, P., Wang, T., & Zhang, P. (2020). Using a pedagogical agent to deliver conversational style instruction: What benefits can you obtain? Computers & Education, 143, 103658.Google Scholar

Louwerse, M. M., Graesser, A. C., Lu, S., & Mitchell, H. H. (2005). Social cues in animated conversational agents. Applied Cognitive Psychology, 19(6), 693–704.Google Scholar

Lusk, M. M., & Atkinson, R. K. (2007). Animated pedagogical agents: Does their degree of embodiment impact learning from static or animated worked examples? Applied Cognitive Psychology, 21(6), 747–764.Google Scholar

Mayer, R. E. (2005). The Cambridge Handbook of Multimedia Learning. New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2014a). Principles based on social cues in multimedia learning: Personalization, voice, image, and embodiment. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 345–370). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2014b). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 43–71). New York: Cambridge University Press.Google Scholar

Mayer, R. E., & DaPra, C. S. (2012). An embodiment effect in computer-based learning with animated pedagogical agents. Journal of Experimental Psychology: Applied, 18(3), 239–252.Google Scholar

Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds? Journal of Educational Psychology, 95(4), 806–812.Google Scholar

Mayer, R. E., & Fiorella, L. (2014). Principle for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity principles. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 279–315). New York: Cambridge University Press.Google Scholar

Mayer, R. E., Sobko, K., & Mautone, P. D. (2003). Social cues in multimedia learning: Role of speaker’s voice. Journal of Educational Psychology, 95(2), 419–425.Google Scholar

Moreno, R., & Flowerday, T. (2006). Students’ choice of animated pedagogical agents in science learning: A test of the similarity–attraction hypothesis on gender and ethnicity. Contemporary Educational Psychology, 31(2), 186–207.CrossRefGoogle Scholar

Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19(2), 177–213.Google Scholar

Moreno, R., Reislein, M., & Ozogul, G. (2010). Using virtual peers to guide visual attention during learning. Journal of Media Psychology: Theories, Methods, and Applications, 22, 52–60.Google Scholar

Moundridou, M., & Virvou, M. (2002). Evaluating the persona effect of an interface agent in a tutoring system. Journal of Computer Assisted Learning, 18(3), 253–261.Google Scholar

Nye, B. D., Graesser, A. C., & Hu, X. (2014). Multimedia learning with intelligent tutoring systems. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 705–728). New York: Cambridge University Press.Google Scholar

Ozogul, G., Johnson, A. M., Atkinson, R. K., & Reisslein, M. (2013). Investigating the impact of pedagogical agent gender matching and learner choice on learning outcomes and perceptions. Computers & Education, 67, 36–50.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, 18(4), 211–229.Google Scholar

Plant, E. A., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle-school students’ attitudes and performance regarding engineering with computer based social models. Computers & Education, 53(2), 209–215.Google Scholar

Rodicio, H. G., & Sánchez, E. (2012). Aids to computer-based multimedia learning: A comparison of human tutoring and computer support. Interactive Learning Environments, 20(5), 423–439.Google Scholar

Schroeder, N. L. (2017). The influence of a pedagogical agent on learners’ cognitive load. Educational Technology & Society, 20(4), 138–147.Google Scholar

Schroeder, N. L., & Adesope, O. O. (2014). A systematic review of pedagogical agents’ persona, motivation, and cognitive load implications for learners. Journal of Research on Technology in Education, 46(3), 229–251.Google Scholar

Schroeder, N. L., Adesope, O. O., & Gilbert, R. B. (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39.Google Scholar

Singer, M., & Goldin-Meadow, S. (2005). Children learn when their teacher’s gestures and speech differ. Psychological Science, 16, 85–89.Google Scholar

van der Meij, H., van der Meij, J., & Harmsen, R. (2015). Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment. Educational Technology Research and Development, 63(3), 381–403.Google Scholar

van Gog, T. (2014). The signaling (or cueing) principle in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 263–278). Cambridge: Cambridge University Press.Google Scholar

van Vugt, H. C., Konijn, E. A., Hoorn, J. F., Keur, I., & Eliëns, A. (2007). Realism is not all! User engagement with task-related. Interacting with Computers, 19, 267–280.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, F., Li, W., Xie, H., & Liu, H. (2017). Is pedagogical agent in multimedia learning good for learning? A meta-analysis. Advances in Psychological Science, 25, 12–28.Google Scholar

Ward, W., Cole, R., & Bolaños, D. (2013). My science tutor: A conversational multimedia virtual tutor. Journal of Educational Psychology, 105(4), 1115–1125.Google Scholar

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636.Google Scholar

Xie, H., Mayer, R. E., Wang, F., & Zhou, Z. (2019). Coordinating visual and auditory cueing in multimedia learning. Journal of Educational Psychology, 111(2), 235–255.Google Scholar

Yilmaz, R., & Kılıç-Çakmak, E. (2012). Educational interface agents as social models to influence learner achievement, attitude and retention of learning. Computers & Education, 59(2), 828–838.Google Scholar

Yung, H. I. (2009). Effects of an animated pedagogical agent with instructional strategies in multimedia learning. Journal of Educational Multimedia and Hypermedia, 18(1), 113–126.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 and Society, 18, 153–160.Google Scholar

Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183–198.Google Scholar

AlZhrani, G., Alotaibi, F., Azarnoush, H. M., Winkler-Schwartz, A., Sabbagh, A., Lajoie, S. P., & Del Maestro, R. F. (2015). Proficiency performance benchmarks for removal of simulated brain tumors using “NeuroTouch” a virtual reality simulator. Journal of Surgical Education, 72(4), 685–696.CrossRefGoogle ScholarPubMed

Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523–535.Google Scholar

Azevedo, R., & Feyzi-Behnagh, R. (2011). Dysregulated learning with advanced learning technologies. Journal of e-Learning and Knowledge Society, 7(2), 9–18.Google Scholar

Azevedo, R., Mudrick, N. V., Taub, M., & Bradbury, A. E. (2019). Self-regulation in computer-assisted learning systems. In Dunlosky, J., & Rawson, K. A. (eds.), The Cambridge Handbook of Cognition and Education (pp. 587–618). Cambridge: Cambridge University Press.Google Scholar

Bannert, M., & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40, 193–211.CrossRefGoogle Scholar

Birchfield, D., Thornburg, H., Megowan-Romanowicz, M. C., Hatton, S., Mechtley, B., Dolgov, I., & Burleson, W. (2008). Embodiment, multimodality, and composition: Convergent themes across HCI and education for mixed-reality learning environments. Advances in Human–Computer Interaction, 2008, 874563.Google Scholar

Bransford, J., Brown, A. L., & Cocking, R. R. (2000). How People Learn: Brain, Mind, Experience, and School (expanded ed.). Commission on Behavioral and Social Sciences and Education, National Research Council. Washington, DC: The National Academies Press.Google Scholar

Burbules, N. C. (2006). Rethinking the virtual. In Weiss, J., Nolan, J., Hunsinger, J., & Trifonas, P. (eds.), The International Handbook of Virtual Learning Environments (pp. 37–58). Dordrecht: Springer.Google Scholar

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology, 3(3), 149–210.Google Scholar

Delorme, S., Laroche, D., DiRaddo, R., & Del Maestro, R. F. (2012). NeuroTouch: A physics-based virtual simulator for cranial microneurosurgery training. Neurosurgery, 71(1 Suppl Operative), 32–42.Google Scholar

Duffy, M. C., Azevedo, R., Sun, N., Griscom, S., Stead, V., Crelinsten, L., Wiseman, J., Maniatis, T., & Lachapelle, K. (2015). Team regulation in a simulated medical emergency: An in-depth analysis of cognitive, metacognitive, and affective processes. Instructional Science, 43, 401–426.Google Scholar

Duffy, M. C., Lajoie, S. P., Pekrun, R., & Lachapelle, K. (2020). Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Journal of Learning and Instruction, 70, 101150.Google Scholar

Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.Google Scholar

Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13(2), 239–244.Google Scholar

Graesser, A. (2020). Emotions are the experiential glue of learning environments in the 21st century. Learning and Instruction, 70, 101212.Google Scholar

Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5–26 .Google Scholar

Hadwin, A. F., Järvelä., S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In Schunk, D. H., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 83–106). Abingdon: Routledge.Google Scholar

Issa, N., Mayer, R. E., Schuller, M., Wang, E., Shapiro, M. B., & Darosa, D. A. (2013). Teaching for understanding in medical classrooms using multimedia design principles. Medical Education, 47(4), 388–396.Google Scholar

Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M.(2016). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction, 43, 39–51.Google Scholar

Järvenoja, H., Järvelä, S., & Malmberg, J. (2020). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, 101090.Google Scholar

Kuang, X., Eysink, T. H., & de Jong, T. (2020). Effects of providing partial hypotheses as a support for simulation‐based inquiry learning. Journal of Computer Assisted Learning, 36(4), 487–501.Google Scholar

Lajoie, S. P. (2014). Multimedia learning of cognitive processes. In Mayer, R. E. (ed.) The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 623–646). Cambridge: Cambridge University Press.Google Scholar

Lajoie, S. P., Cruz-Panesso, I., & Lachapelle, K. (2015). Learning in the health sector with simulated systems. In Spector, M. (ed.), Encyclopedia of Educational Technology (pp. 470–472). Thousand Oaks, CA: Sage.Google Scholar

Lajoie, S. P. & Li, S. (submitted). Interface designs applied to AIED learning and teaching environments. In. du Boulay, B., Mitrovic, A., & Yacef, K. (eds.), Handbook of Artificial Intelligence in Education. Cheltenham: Edward Elgar Press.Google Scholar

Lajoie, S. P., & Nakamura, C. (2005). Multimedia learning of cognitive skills. In Mayer, R. (ed.), Cambridge Handbook of Multimedia Learning (pp. 489–504). Cambridge: Cambridge University Press.Google Scholar

Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2020). Understanding and measuring emotions in technology-rich learning environments. Journal of Learning and Instruction, 70, 101272.Google Scholar

Lajoie, S. P., & Poitras, E. (2017). Crossing disciplinary boundaries to improve technology rich learning. Teachers College Record, 119(3), 1–30.Google Scholar

Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments, Learning and Instruction, 70, 101162.Google Scholar

Liu, C., Calvo, R., & Lim, R. (2016b). Improving medical students’ awareness of their nonverbal communication through automated nonverbal behavior feedback. Frontiers in ICT, 3(11).Google Scholar

Liu, C., Lim, R., McCabe, K., Taylor, S., & Calvo, R. (2016a). A web-based telehealth training platform incorporating automated non-verbal behavior feedback for teaching communication skills to medical students: A randomized crossover study. Journal of Medical Internet Research, 18(9), e246.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

Mavin, T. J., & Murray, P. S. (2010). The development of airline pilot skills through simulated practice. In Billett, S. (ed.), Learning through Practice. Professional and Practice-based Learning (Vol 1, pp. 268–286). Heidelberg: Springer.Google Scholar

Mayer, R. E. (2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 13(2), 125–139.Google Scholar

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

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

Mayer, R. E. (2020b). Searching for the role of emotions in e-learning. Learning and Instruction, 70, 101213.Google Scholar

Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual-processing systems in working memory. Journal of Educational Psychology, 90(2), 312–320.Google Scholar

McLean, G. M., Lambeth, S., & Mavin, T. (2016). The use of simulation in ab initio pilot training. The International Journal of Aviation Psychology, 26(1–2), 36–45.Google Scholar

Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers and Education, 70, 29–40.Google Scholar

Merriam-Webster. (n.d.). Simulation. In Merriam-Webster.com dictionary. Available from www.merriam-webster.com/dictionary/simulation (last accessed September 22, 2020).Google Scholar

Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., & Del Maestro, R. F. (2020). The virtual operative assistant: An explainable artificial intelligence tool for simulation based training in surgery and medicine. PLoS ONE, 15(2), e0229596.Google Scholar

Olympiou, G., Zacharias, Z., & Dejong, T. (2013). Making the invisible visible: Enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation. Instructional Science, 41(3), 575–596.Google Scholar

Papert, S. (1987), Microworlds: Transforming education. In Lawler, R., & Yazsani, M. (eds.), Artificial Intelligence and Education Learning Environments and Tutoring Systems (pp. 79–94). New York: Ablex Publishers.Google Scholar

Parong, J., & Mayer, R. E. (2021). Cognitive and affective processes for learning science in immersive virtual reality. Journal of Computer Assisted Learning, 37, 226–241.Google Scholar

Pekrun, R., & Perry, R. P. (2014). Control value theory of achievement emotions. In Pekrun, R., & Linnenbrink-Garcia, L. (eds.), International Handbook of Emotions in Education (pp. 120–141). New York: Routledge.Google Scholar

Platts, D., Anderson, B., Forshaw, T., & Burstow, D. (2011). Use of an echocardiographic mannequin simulator for early-sonographer training. Heart, Lung and Circulation, 20, S199–S200.Google Scholar

Reed, S. K. ( 2010). Cognitive architectures for multimedia learning. Educational Psychologist, 41(2), 87–98.Google Scholar

Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21(1–2), 115–133.Google Scholar

Sabourin, J. L., Rowe, J. P., Mott, B. W., & Lester, J. C. (2013). Considering alternate futures to classify off-task behavior as emotion self-regulation: A supervised learning approach. Journal of Educational Data Mining, 5(1), 9–38.Google Scholar

Salas, E., Bowers, C. A., & Rhodenizer, L. (1998). It is not how much you have but how you use it: Toward a rational use of simulation to support aviation training. The International Journal of Aviation Psychology, 8(3), 197–208.Google Scholar

Shute, V., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C., Kuba, R., Liu, Z., Yang, X., & Sun, C. (2021). Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. Journal of Computer Assisted Learning, 37, 127–141.CrossRefGoogle Scholar

Shute, V., & Ventura, M. (2013). Measuring and Supporting Learning in Games: Stealth Assessment. Cambridge, MA: The MIT Press.Google Scholar

Sroka, G., Feldman, L. S., Vassiliou, M. C., Kaneva, P. A., Fayez, R., & Fried, G. M. (2010). Fundamentals of laparoscopic surgery simulator training to proficiency improves laparoscopic performance in the operating room – A randomized controlled trial. American Journal of Surgery, 199(1), 115–120.Google Scholar

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.Google Scholar

Taub, M., Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. (2020). The agency effect: The impact of student agency on learning, emotions, and problem-solving behaviors in a game-based learning environment. Computers & Education, 147, 103781.Google Scholar

Vassiliou, M. C., Feldman, L. S., Andrew, C. G., Bergman, S., Leffondré, K., Stanbridge, D., & Fried, G. M. (2005). A global assessment tool for evaluation of intraoperative laparoscopic skills. American Journal of Surgery, 190, 107–113.Google Scholar

Wiseman, J., Blanchard, E. G., & Lajoie, S. P. (2016). The deteriorating patient smartphone app: Towards serious game design. In Bridges, S., Chan, L. K., & Hmelo-Silver, C. (eds.), Educational Technologies in Medical and Health Sciences Education (pp. 215-234). New York: Springer.Google Scholar

Wiseman, J., & Snell, L. (2008). The deteriorating patient: A realistic but “low‐tech” simulation of emergency decision‐making. Clinical Teacher, 5(2), 93–97.Google Scholar

Abt, C. C. (1970). Serious Games. New York: Viking.Google Scholar

Adams, D. M., Mayer, R. E., MacNamara, A., Koening, A., & Wainess, R. (2012). Narrative games for learning: Testing the discovery and narrative hypothesis. Journal of Educational Psychology, 104, 235–249.Google Scholar

Adams, D. M., Pilegard, C., & Mayer, R. E. (2016). Evaluating the cognitive consequences of playing Portal for a short duration. Journal of Educational Computing Research, 54, 173–195.Google Scholar

Anguera, J. A., Boccanfuso, J., Rintoul, J. L., Al-Hashimi, O., Faraji, F., Janowich, J., Kong, E., Larraburo, Y., Rolle, C., Johnston, E., and Gazzaley, A. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97–101.Google Scholar

Bainbridge, K., & Mayer, R. E. (2018). Shining the light of research on lumosity. Journal of Cognitive Enhancement, 2, 43–62.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(1), 77–110.Google Scholar

Cameron, B., & Dwyer, F. (2005). The effect of online gaming, cognition and feedback type in facilitating delayed achievement of different learning objectives. Journal of Interactive Learning Research, 16, 243–258.Google Scholar

Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86, 79–122.Google Scholar

Clark, R. E. (2001). Learning from Media. Greenwich, CT: Information Age Publishing.Google Scholar

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar

Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic review of empirical evidence on computer games and serious games. Computers & Education, 59, 661–686.Google Scholar

DeLeeuw, K., & Mayer, R. E. (2011). Cognitive consequences of making computer-based learning activities more game-like. Computers in Human Behavior, 27, 2011–2016.Google Scholar

Fiorella, L., & Mayer, R. E. (2012). Paper-based aids for learning with a computer-based game. Journal of Educational Psychology, 104, 1074–1082.Google Scholar

Fiorella, L., & Mayer, R. E. (2015). Learning As a Generative Activity. New York: Cambridge University Press.Google Scholar

Hardy, J. L., Nelson, R. A., Thomason, M. E., Sternberg, D. A., Katovich, K., Farzin, , & Scanlon, M. (2015). Enhancing cognitive abilities with comprehensive training: A large, online, randomized, active-controlled trial. PLoS ONE, 10(9), e0134467.Google Scholar

Honey, M., & Hilton, M. (eds.). (2011). Learning Science through Computer Games and Simulations. Washington, DC: National Academy Press.Google Scholar

James, K., & Mayer, R. E. (2019). Learning a second language by playing a game. Applied Cognitive Psychology, 33, 669–674.Google Scholar

Johnson, C. I., & Mayer, R. E. (2010). Adding the self-explanation principle to multimedia learning in a computer-based game-like environment. Computers in Human Behavior, 26, 1246–1252.Google Scholar

Kable, J. W., Caufield, M. K., Falcone, M., McConnell, M., Bernardo, L., Parthasarathi, T., ... & Lerman, C. (2017). No effect of commercial cognitive training on brain activity, choice behavior, or cognitive performance. Journal of Neuroscience, 37(31), 7390–7402.Google Scholar

Leutner, D. (1993). Guided discovery learning with computer-based simulation games: Effects of adaptive and non-adaptive instructional support. Learning and Instruction, 3, 113–132.Google Scholar

Loftus, G. R., & Loftus, E. F. (1983). Mind at Play: The Psychology of Video Games. New York: Basic Books.Google Scholar

Lorant-Royer, S., Munch, C., Mescle, H., & Lieury, A. (2010). Kawashima vs “Super Mario”! Should a game be serious in order to stimulate cognitive aptitudes? European Review of Applied Psychology, 60 (4), 221–232.Google Scholar

Mayer, R. E. (2011). Applying the Science of Learning. Boston: Pearson.Google Scholar

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

Mayer, R. E. (2016). What should be the role of computer games in education? Policy Insights from Behavioral and Brain Sciences, 3(1), 20–26.Google Scholar

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

Mayer, R. E. (2019b). Cognitive foundations of game-based learning. In Plass, J., Homer, B., & Mayer, R. E. (eds.), Handbook of Game-based Learning (pp. 83–110). Cambridge, MA: MIT Press.Google Scholar

Mayer, R. E., & Johnson, C. I. (2010). Adding instructional features that promote learning in a game-like environment. Journal of Educational Computing Research, 42, 241–265.Google Scholar

Mayer, R. E., Mautone, P. D., & Prothero, W. (2002). Pictorial aids for learning by doing in a multimedia geology simulation game. Journal of Educational Psychology, 94, 171–185.Google Scholar

Mayer, R. E., Parong, J., & Bainbridge, K. (2019). Young adults learning executive function skills by playing focused video games. Cognitive Development, 49, 43–50.Google Scholar

McGonigal, J. (2011). Reality Is Broken: Why Games Make Us Better and How They Can Change the World. New York: Penguin Press.Google Scholar

McLaren, B. M., Adams, D., Mayer, R., & Forlizzi, J. (2017). Decimal point: An educational game that benefits mathematics learning more than a conventional approach. International Journal of Game-Based Learning, 7(1), 36–56.Google Scholar

Moreno, R., & Mayer, R. E. (2000). Engaging students in active learning: The case for personalized multimedia messages. Journal of Educational Psychology, 93, 724–733.Google Scholar

Moreno, R., & Mayer, R. E. (2002a). Verbal redundancy in multimedia learning: When reading helps listening. Journal of Educational Psychology, 94, 156–163.Google Scholar

Moreno, R., & Mayer, R. E. (2002b). Learning science in virtual reality environments: Role of methods and media. Journal of Educational Psychology, 94, 598–610.Google Scholar

Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96, 165–173.Google Scholar

Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent-based multimedia game. Journal of Educational Psychology, 97, 117–128.Google Scholar

Moreno, R., Mayer, R. E., Spires, H., & Lester, J. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19, 177–214.Google Scholar

Nouchi, R., Yasuyuki, T., Takeuchi, H., Hashizume, H., Akitsuki, Y., Shigemune, Y., Sekiguchi, A., Kotozaki, Y., Tsukiura, T., Yomogida, Y., & Kawashima, R. (2012). Brain training game improves executive functions and processing speed in the elderly: A randomized controlled trial. PLoS ONE, 7(1), e29676.Google Scholar

O’Neil, H. F., Chung, G., Kerr, D., Vendlinski, T., Bushchang, R., & Mayer, R. E. (2014). Adding self-explanation prompts to an educational game. Computers in Human Behavior, 30, 23–28.Google Scholar

O’Neil, H. F., & Perez, R. S. (eds.) (2008). Computer Games and Team and Individual Learning. Amsterdam: Elsevier.Google Scholar

Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., Howard, R. J., & Ballard, C. G. (2010). Putting brain training to the test. Nature, 465(7299), 775–778.Google Scholar

Parong, J., & Mayer, R. E. (2020). Cognitive consequences of playing brain training games in virtual reality. Applied Cognitive Psychology, 34, 29–38.Google Scholar

Parong, J., Mayer, R. E., Fiorella, L., MacNamara, A., Plass, J., & Homer, B. (2017). Learning executive function skills by playing focused video games. Contemporary Educational Psychology, 51, 141–151.Google Scholar

Parong, J., Wells, A., & Mayer, R. E. (2020). Replicated evidence towards a cognitive theory of game-based training. Journal of Educational Psychology, 112, 922–937.Google Scholar

Pilegard, C., & Mayer, R. E. (2016). Improving academic learning from computer-based narrative games. Contemporary Educational Psychology, 44, 12–20.Google Scholar

Pilegard, C., & Mayer, R. E. (2018). Game over for Tetris as a platform for cognitive skill training. Contemporary Educational Psychology, 54, 29–41.Google Scholar

Plass, J., Mayer, R. E., & Homer, B. (eds.) (2019). Handbook of Game-based Learning. Cambridge, MA: MIT Press.Google Scholar

Shavelson, R. J., & Towne, L. (eds.) (2002). Scientific Research in Education. Washington, DC: National Academy Press.Google Scholar

Simons, D. J., Boot, W. R., Charbness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do brain training programs work? Psychological Science in the Public Interest, 17(3), 103–186.Google Scholar

Sims, V. K., & Mayer, R. E. (2002). Domain specificity of spatial expertise: The case of video game players. Applied Cognitive Psychology, 16, 97–115.Google Scholar

Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64, 489–528.Google Scholar

Tobias, S., & Fletcher, J. D. (eds.) (2011). Computer Games and Instruction. Charlotte, NC: Information Age Publishing.Google Scholar

Tobias, S., Fletcher, J. D., Dai, D. Y., & Wind, A. P. (2011). Review of research on computer games. In Tobias, S., & Fletcher, J. D. (eds.), Computer Games and Instruction (pp. 525–545). Charlotte, NC: Information Age Publishing.Google Scholar

van Eck, R., & Dempsey, J. (2002). The effect of competition and contextualized advisement on the transfer of mathematics skills in a computer-based instructional simulation game. Educational Technology Research and Development, 50, 23–41.Google Scholar

Vogel, J. J., Vogel, D. S., Cannon-Bowers, J., Bowers, C. A., Muse, K., & Wright, M. (2006). Computer gaming and interactive simulations for learning: A meta-analysis. Journal of Educational Computing Research, 34, 229–243.Google Scholar

Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., & Collins, H. (2008). The politeness effect: Pedagogical agents and learning outcomes. International Journal of Human Computer Studies, 66, 96–112.Google Scholar

Wells, A., Parong, J., & Mayer, R. E. (2021). Limits on training inhibitory control with a focused video game. Journal of Cognitive Enhancement, 5, 83–98.Google Scholar

Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105, 249–265.Google Scholar

Wouters, P., & van Oostendorp, H. (eds.) (2017). Instructional Techniques to Facilitate Learning and Motivation of Serious Games. New York: Springer.Google Scholar

Young, M. F., Slota, S., Cutter, A. B., Jalette, G., Mullin, G., Lai, B., Simeoni, Z., Tran, M., & Yukhymenko, M. (2012). Our princess is in another castle: A review of trends in serious gaming for education. Review of Educational Research, 82, 61–89.Google Scholar

Ainsworth, S., & Loizou, A. (2003). The effects of self‐explaining when learning with text or diagrams. Cognitive Science, 27(4), 669–681.Google Scholar

Atkinson, R. K., Mayer, R. E., & Merrill, M. M. (2005). Fostering social agency in multimedia learning: Examining the impact of an animated agent’s voice. Contemporary Educational Psychology, 30, 117–139.Google Scholar

Beege, M., Schneider, S., Nebel, S., & Rey, G. D. (2017). Look into my eyes! Exploring the effect of addressing in educational videos. Learning and Instruction, 49, 113–120.Google Scholar

Betrancourt, M., & Benetos, K. (2018). Why and when does instructional video facilitate learning? A commentary to the special issue “Developments and trends in learning with instructional video.” Computers in Human Behavior, 89, 471–475.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

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In Gernsbacher, M. A., Pew, R. W., Hough, L. M., & Pomerantz, J. R. (eds.) & FABBS Foundation, Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society (pp. 56–64). New York: Worth Publishers.Google Scholar

Bonk, C. J., Lee, M. M., Reeves, T. C., & Reynolds, T. H. (2015). MOOCs and Open Education around the World. New York: Routledge.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

Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into EdX’s first MOOC. Research and Practice in Assessment, 8, 13–25.Google Scholar

Carpenter, S. K., & Toftness, A. R. (2017). The effect of prequestions on learning from video presentations. Journal of Applied Research in Memory and Cognition, 6(1), 104–109.Google Scholar

Colliot, T., & Jamet, E. (2018). Understanding the effects of a teacher video on learning from a multimedia document: An eye-tracking study. Educational Technology Research and Development, 66(6), 1415–1433.Google Scholar

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

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

DeLozier, S. J., & Rhodes, M. G. (2017). Flipped classrooms: A review of key ideas and recommendations for practice. Educational Psychology Review, 29(1), 141–151.Google Scholar

Fiorella, L. (2020). The science of habit and its implications for student learning and well-being. Educational Psychology Review, 32, 603–625.Google Scholar

Fiorella, L., & Mayer, R. E. (2015). Learning As a Generative Activity. New York: Cambridge University Press.Google Scholar

Fiorella, L., & Mayer, R. E. (2016a). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717–741.Google Scholar

Fiorella, L., & Mayer, R. E. (2016b). Effects of observing the instructor draw diagrams on learning from multimedia messages. Journal of Educational Psychology, 108(4), 528–546.Google Scholar

Fiorella, L., & Mayer, R. E. (2017). Spontaneous spatial strategy use in learning from scientific text. Contemporary Educational Psychology, 49, 66–79.Google Scholar

Fiorella, L., & Mayer, R. E. (2018). What works and doesn’t work with instructional video. Computers in Human Behavior, 89, 465–470.Google Scholar

Fiorella, L., & Zhang, Q. (2018). Drawing boundary conditions for learning by drawing. Educational Psychology Review, 30, 1115–1137.Google Scholar

Fiorella, L., Stull, A. T., Kuhlmann, S., & Mayer, R. E. (2019). Instructor presence in video lectures: The role of dynamic drawings, eye contact, and instructor visibility. Journal of Educational Psychology, 111(7), 1162–1171.Google Scholar

Fiorella, L., Stull, A. T., Kuhlmann, S., & Mayer, R. E. (2020). Fostering generative learning from video lessons: Benefits of instructor-generated drawings and learner-generated explanations. Journal of Educational Psychology, 112(5), 895–906.Google Scholar

Fiorella, L., van Gog, T., Hoogerheide, V., & Mayer, R. E. (2017). It’s all a matter of perspective: Viewing first-person video modeling examples promotes learning of an assembly task. Journal of Educational Psychology, 109, 653–665.Google Scholar

Fries, L., DeCaro, M. S., & Ramirez, G. (2019). The lure of seductive details during lecture learning. Journal of Educational Psychology, 111(4), 736–749.Google Scholar

Garland, T. B., & Sanchez, C. A. (2013). Rotational perspective and learning procedural tasks from dynamic media. Computers & Education, 69, 31–37.Google Scholar

Gorissen, P., van Bruggen, J., & Jochems, W. (2012). Students and recorded lectures: Survey on current use and demands for higher education. Research in Learning Technology, 20, 297–311.Google Scholar

Hasler, B. S., Kersten, B., & Sweller, J. (2007). Learner control, cognitive load and instructional animation. Applied Cognitive Psychology, 21(6), 713–729.Google Scholar

Homer, B. D., Plass, J. L., & Blake, L. (2008). The effects of video on cognitive load and social presence in multimedia-learning. Computers in Human Behavior, 24(3), 786–797.Google Scholar

Hoogerheide, V., Loyens, S. M., & van Gog, T. (2016). Learning from video modeling examples: Does gender matter? Instructional Science, 44(1), 69–86.Google Scholar

Hoogerheide, V., van Wermeskerken, M., Loyens, S. M., & van Gog, T. (2016). Learning from video modeling examples: Content kept equal, adults are more effective models than peers. Learning and Instruction, 44, 22–30.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

Jing, H. G., Szpunar, K. K., & Schacter, D. L. (2016). Interpolated testing influences focused attention and improves integration of information during a video-recorded lecture. Journal of Experimental Psychology: Applied, 22(3), 305.Google Scholar

Kane, M. J., Smeekens, B. A., von Bastian, C. C., Lurquin, J. H., Carruth, N. P., & Miyake, A. (2017). A combined experimental and individual-differences investigation into mind wandering during a video lecture. Journal of Experimental Psychology: General, 146(11), 1649–1674.Google Scholar

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299.Google Scholar

Karpicke, J. D. (2012). Retrieval-based learning: Active retrieval promotes meaningful learning. Current Directions in Psychological Science, 21(3), 157–163.Google Scholar

Karpicke, J. D., Butler, A. C., & Roediger, H. L. III (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory, 17(4), 471–479.Google Scholar

Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28, 820–831.Google Scholar

Kizilcec, R. F., Bailenson, J. N., & Gomez, C. J. (2015). The instructor’s face in video instruction: Evidence from two large-scale field studies. Journal of Educational Psychology, 107(3), 724–739.Google Scholar

Lee, H., & Mayer, R. E. (2018). Fostering learning from instructional video in a second language. Applied Cognitive Psychology, 32, 648–654.Google Scholar

Lindgren, R. (2012). Generating a learning stance through perspective-taking in a virtual environment. Computers in Human Behavior, 28(4), 1130–1139.Google Scholar

Loh, K. K., Tan, B. Z. H., & Lim, S. W. H. (2016). Media multitasking predicts video-recorded lecture learning performance through mind wandering tendencies. Computers in Human Behavior, 63, 943–947.Google Scholar

Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? Journal of Educational Psychology, 93(2), 390–397.Google Scholar

Mayer, R. E., Fiorella, L., & Stull, A. (2020). Five ways to increase the effectiveness of instructional video. Educational Technology Research & Development, 68(3), 837–852.Google Scholar

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198.Google Scholar

Merkt, M., Weigand, S., Heier, A., & Schwan, S. (2011). Learning with videos vs. learning with print: The role of interactive features. Learning and Instruction, 21(6), 687–704.Google Scholar

Moreno, R. (2007). Optimising learning from animations by minimizing cognitive load: Cognitive and affective consequences of signaling and segmentation methods. Applied Cognitive Psychology, 21(6), 765–781.Google Scholar

O’Callagan, F. V., Neumann, D. L., Jones, L., & Creed, P. A. (2017). The use of lecture recordings in higher education: A review of institutional, student, and lecturer issues. Education and Information Technologies, 22, 399–415.Google Scholar

Ouwehand, K., van Gog, T., & Paas, F. (2015a). Designing effective video-based modeling examples using gaze and gesture cues. Educational Technology & Society (online), 18, 78–88.Google Scholar

Ouwehand, K., van Gog, T., & Paas, F. (2015b). Effects of gestures on older adults’ learning from video‐based models. Applied Cognitive Psychology, 29(1), 115–128.Google Scholar

Ozogul, G., Johnson, A., Atkinson, R. K., & Reisslein, M. (2013). Investigating the impact of pedagogical agent gender matching and learner choice on learning outcomes and perceptions. Computers & Education, 67, 36–50.Google Scholar

Pi, Z., Hong, J., & Yang, J. (2017). Effects of the instructor’s pointing gestures on learning performance in video lectures. British Journal of Educational Technology, 48(4), 1020–1029.Google Scholar

Pi, Z., Zhang, Y., Zhu, F., Xu, K., Yang, J., & Hu, W. (2019). Instructors’ pointing gestures improve learning regardless of their use of directed gaze in video lectures. Computers & Education, 128, 345–352.Google Scholar

Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1–29.Google Scholar

Rey, G. D. (2012). A review of research and a meta-analysis of the seductive detail effect. Educational Research Review, 7(3), 216–237.Google Scholar

Roediger, H. L. III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255.Google Scholar

Schroeder, N. L., & Traxler, A. L. (2017). Humanizing instructional videos in physics: When less is more. Journal of Science Education and Technology, 26(3), 269–278.Google Scholar

Schunk, D. H., & Hanson, A. R. (1985). Peer models: Influence on children’s self-efficacy and achievement. Journal of Educational Psychology, 77, 313–322.Google Scholar

Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759–775.Google Scholar

Shen, B., McCaughtry, N., Martin, J., & Dillion, S. (2006). Does “sneaky fox” facilitate learning? Examining the effects of seductive details in physical education. Research Quarterly for Exercise and Sport, 77(4), 498–506.Google Scholar

Spanjers, I. E. A., van Gog, T., Wouters, P., & van Merrienboer, J. J. G. (2012). Explaining the segmentation effect in learning form animations: The role of pausing and temporal cueing. Computers & Education, 59(2), 274–280.Google Scholar

Spanjers, I. E. A., Wouters, P., van Gog, T., & van Merrienboer, J. J. G. (2011). An expertise reversal effect for segmentation in learning from animated worked-out examples. Computers in Human Behavior, 27(1), 46–52.Google Scholar

Stull, A. T., Fiorella, L., Gainer, M. J., & Mayer, R. E. (2018). Using transparent whiteboards to boost learning from online STEM lectures. Computers & Education, 120, 146–159.Google Scholar

Stull, A. T., Fiorella, L., & Mayer, R. E. (2018). An eye-tracking analysis of instructor presence in video lectures. Computers in Human Behavior, 88, 263–272.Google Scholar

Sundararajan, N., & Adesope, O. (2020). Keep it coherent: A meta-analysis of the seductive details effect. Educational Psychology Review, 32, 707–734.Google Scholar

Szpunar, K. K., Jing, H. G., & Schacter, D. L. (2014). Overcoming overconfidence in learning from video-recorded lectures: Implications of interpolated testing for online education. Journal of Applied Research in Memory and Cognition, 3(3), 161–164.Google Scholar