Atkinson, R. K. (2002). Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology, 94, 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, 117–139.Google Scholar

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

Brom, C., Bromová, E., Děchtěrenko, F., Buchtová, M., & Pergel, M. (2014). Personalized messages in a brewery educational simulation: Is the personalization principle less robust than previously thought? Computers & Education, 72, 339–366.Google Scholar

Brom, C., Hannemann, T., Stárková, T., Bromová, E., & Děchtěrenko, F. (2017). The role of cultural background in the personalization principle: Five experiments with Czech learners. Computers & Education, 112, 37–68.Google Scholar

Brown, P., & Levinson, S. C. (1987). Politeness: Some Universals in Language Usage. New York: Cambridge University Press.Google Scholar

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

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

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

Craig, S. D., & Schroeder, N. L. (2017). Reconsidering the voice effect when learning from a virtual human. Computers & Education, 114, 193–205.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, 22, 84–97.Google Scholar

Dunsworth, Q., & Atkinson, R. K. (2010). Fostering multimedia learning of science: Explaining the role of an animated agent’s image. Computers & Education, 49, 677–690.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

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

Ginns, P., & Fraser, J. (2010). Personalization enhances learning anatomy terms. Medical Teacher, 32(9), 776–778.Google Scholar

Ginns, P., Marin, A. J., & Marsh, H. M. (2013). Designing instructional text for conversational style: A meta-analysis. Educational Psychology Review, 25(4), 445–472.Google Scholar

Grice, H. P. (1975). Logic and conversation. In Cole, P., & Morgan, J. (eds.), Syntax and Semantics (Vol. 3, pp. 41–58). New York: Academic Press.Google Scholar

Guo, Y. R., & Goh, D. H. L. (2015). Affect in embodied pedagogical agents: Meta-analytic review. Journal of Educational Computing Research, 53(1), 124–149.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

Kartal, G. (2010). Does language matter in multimedia learning? Personalization principle revisited. Journal of Educational Psychology, 102, 615–624.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

Kühl, T., & Zander, S. (2017). An inverted personalization effect when learning with multimedia: The case of aversive content. Computers & Education, 108, 71–84.Google Scholar

Li, J., Kizilcec, R., Bailenson, J., & Ju, W. (2016). Social robots and virtual agents as lecturers for video instruction. Computers in Human Behavior, 55, 1222–1230.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

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, 747–764.Google Scholar

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

Mayer, R. E., Dow, G., & Mayer, R. E. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds? Journal of Educational Psychology, 95, 806–813.Google Scholar

Mayer, R. E., Fennell, S., Farmer, L., & Campbell, J. (2004). A personalization effect in multimedia learning: Students learn better when words are in conversational style rather than formal style. Journal of Educational Psychology, 96, 389–395.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, 419–425.Google Scholar

McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011a). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56, 574–584.Google Scholar

McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011b). A politeness effect in learning with web-based intelligent tutors. International Journal of Human–Computer Studies, 69, 70–79.Google Scholar

McLaren, B. M., Lim, S., Yaron, D., & Koedinger, K. (2007). Can a polite intelligent tutoring system lead to improved learning outside the lab? In Proceedings of the 13th International Conference on Artificial Intelligence in Education (pp. 433–440). Amsterdam: IOS Press.Google Scholar

Moreno, R., & Mayer, R. E. (2000). Engaging students in active learning: The case for personalized multimedia messages. Journal of Educational Psychology, 92, 724–733.Google Scholar

Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning invirtual environments. Journal of Educational Psychology, 96, 165–173.Google 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, 177–213.Google Scholar

Moreno, R., Reislein, M., & Ozogul, G. (2010). Using virtual peers to guide visual attention during learning: A test of the persona hypothesis. Journal of Media Psychology, 22, 52–60.Google Scholar

Nass, C., & Brave, S. (2005). Wired for Speech. Cambridge, MA: MIT Press.Google Scholar

Reeves, B., and Nass, C. (1996). The Media Equation. New York: Cambridge University Press.Google Scholar

Reichelt, M., Kämmerer, F., Niegemann, H. M., & Zander, S. (2014). Talk to me personally: Personalization of language style in computer-based learning. Computers in Human Behavior, 35, 199–210.Google Scholar

Schneider, S., Nebel, S., Pradel, S., & Rey, G. D. (2015a). Mind your Ps and Qs! How polite instructions affect learning with multimedia. Computers in Human Behavior, 51, 546–555.Google Scholar

Schneider, S., Nebel, S., Pradel, S., & Rey, G. D. (2015b). Introducing the familiarity mechanism: A unified explanatory approach for the personalization effect and the examination of youth slang in multimedia learning. Computers in Human Behavior, 43, 129–138.Google Scholar

Schrader, C., Reichelt, M., & Zander, S. (2018). The effect of the personalization principle on multimedia learning: The role of student individual interests as a predictor. Educational Technology Research and Development, 66(6), 1387–1397.Google Scholar

van Gog, T., Verveer, I., & Verveer, L. (2014). Learning from video modeling examples: Effects of seeing the human model’s face. Computers & Education, 72, 323–327.Google Scholar

van Wermeskerken, M., Grimmius, B., & van Gog, T. (2018). Attention to the model’s face when learning from video modeling examples in adolescents with and without autism spectrum disorder. Journal of Computer Assisted Learning, 34(1), 32–41.Google Scholar

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

Wang, F., 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, J., & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning. Computers in Human Behavior, 71, 79–89.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, 98–112.Google Scholar

Wilson, K. E., Martinez, M., Mills, C., D’Mello, S., Smilek, D., & Risko, E. F. (2018). Instructor presence effect: Liking does not always lead to learning. Computers & Education, 122, 205–220.Google Scholar

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

Zander, S., Wetzel, S., Kühl, T., & Bertel, S. (2017). Underlying processes of an inverted personalization effect in multimedia learning – An eye-tracking study. Frontiers in Psychology, 8, 2202.Google Scholar

Agostinho, S., Tindall-Ford, S., Ginns, P., Howard, S. J., Leahy, W., & Paas, F. (2015). Giving learning a helping hand: Finger tracing of temperature graphs on an iPad. Educational Psychology Review, 27(3), 427–443.Google Scholar

Alibali, M. W., & Nathan, M. J. (2012). Embodiment in mathematics teaching and learning: Evidence from learners’ and teachers’ gestures. Journal of the Learning Sciences, 21(2), 247–286.Google Scholar

Ayres, P., Marcus, N., Chan, C., & Qian, N. (2009). Learning hand manipulative tasks: When instructional animations are superior to equivalent static representations. Computers in Human Behavior, 25(2), 348–353.Google Scholar

Barrett, T. J., Stull, A. T., Hsu, T. M., & Hegarty, M. (2015). Constrained interactivity for relating multiple representations in science: When virtual is better than real. Computers & Education, 81, 69–81.Google Scholar

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.Google Scholar

Broaders, S. C., Cook, S. W., Mitchell, Z., & Goldin-Meadow, S. (2007). Making children gesture brings out implicit knowledge and leads to learning. Journal of Experimental Psychology: General, 136(4), 539–550.Google Scholar

Brooks, N., & Goldin‐Meadow, S. (2016). Moving to learn: How guiding the hands can set the stage for learning. Cognitive Science, 40(7), 1831–1849.Google Scholar

Boucheix, J. M., & Forestier, C. (2017). Reducing the transience effect of animations does not (always) lead to better performance in children learning a complex hand procedure. Computers in Human Behavior, 69, 358–370.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

Carbonneau, K. J., Marley, S. C., & Selig, J. P. (2013). A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives. Journal of Educational Psychology, 105(2), 380.Google Scholar

Castro-Alonso, J. C., Ayres, P., & Paas, F. (2014). Learning from observing hands in static and animated versions of non-manipulative tasks. Learning and Instruction, 34, 11–21.Google Scholar

Castro-Alonso, J. C., Ayres, P., & Paas, F. (2016). Comparing apples and oranges? A critical look at research on learning from statics versus animations. Computers & Education, 102, 234–243.Google Scholar

Cherdieu, M., Palombi, O., Gerber, S., Troccaz, J., & Rochet-Capellan, A. (2017). Make gestures to learn: Reproducing gestures improves the learning of anatomical knowledge more than just seeing gestures. Frontiers in Psychology, 8, 1689.Google Scholar

Congdon, E. L., Novack, M. A., Brooks, N., Hemani-Lopez, N., O’Keefe, L., & Goldin-Meadow, S. (2017). Better together: Simultaneous presentation of speech and gesture in math instruction supports generalization and retention. Learning and Instruction, 50, 65–74.Google Scholar

Cook, S. W., Duffy, R. G., & Fenn, K. M. (2013). Consolidation and transfer of learning after observing hand gesture. Child Development, 84(6), 1863–1871.Google Scholar

Cook, S. W., Mitchell, Z., & Goldin-Meadow, S. (2008). Gesturing makes learning last. Cognition, 106(2), 1047–1058.Google Scholar

de Koning, B. B., & Tabbers, H. K. (2011). Facilitating understanding of movements in dynamic visualizations: An embodied perspective. Educational Psychology Review, 23(4), 501–521.Google Scholar

de Koning, B. B., & Tabbers, H. K. (2013). Gestures in instructional animations: A helping hand to understanding non‐human movements? Applied Cognitive Psychology, 27(5), 683–689.Google Scholar

de Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2010). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20(2), 111–122.Google Scholar

Du, X., & Zhang, Q. (2019). Tracing worked examples: Effects on learning in geometry. Educational Psychology, 39(2), 169–187.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.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.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

Fujimura, N. (2001). Facilitating children’s proportional reasoning: A model of reasoning processes and effects of intervention on strategy change. Journal of Educational Psychology, 93(3), 589–603.Google Scholar

Fyfe, E. R., McNeil, N. M., Son, J. Y., & Goldstone, R. L. (2014). Concreteness fading in mathematics and science instruction: A systematic review. Educational Psychology Review, 26(1), 9–25.Google Scholar

Ganier, F., & de Vries, P. (2016). Are instructions in video format always better than photographs when learning manual techniques? The case of learning how to do sutures. Learning and Instruction, 44, 87–96.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

Ginns, P., Hu, F. T., Byrne, E., & Bobis, J. (2016). Learning by tracing worked examples. Applied Cognitive Psychology, 30(2), 160–169.Google Scholar

Glenberg, A. M. (2008). Embodiment for education. In Calvo, P., & Gamila, T. (eds.), Handbook of Cognitive Science (pp. 355–372). Amsterdam: Elsevier.Google Scholar

Glenberg, A. M., Goldberg, A. B., & Zhu, X. (2011). Improving early reading comprehension using embodied CAI. Instructional Science, 39(1), 27–39.Google Scholar

Glenberg, A. M., Gutierrez, T., Levin, J. R., Japuntich, S., & Kaschak, M. P. (2004). Activity and imagined activity can enhance young children’s reading comprehension. Journal of Educational Psychology, 96(3), 424–436.Google Scholar

Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. Psychonomic Bulletin & Review, 9(3), 558–565.Google Scholar

Glenberg, A. M., Witt, J. K., & Metcalfe, J. (2013). From the revolution to embodiment: 25 years of cognitive psychology. Perspectives on Psychological Science, 8(5), 573–585.Google Scholar

Goldin-Meadow, S., Cook, S. W., & Mitchell, Z. A. (2009). Gesturing gives children new ideas about math. Psychological Science, 20(3), 267–272.Google Scholar

Goldin‐Meadow, S., Levine, S. C., Zinchenko, E., Yip, T. K., Hemani, N., & Factor, L. (2012). Doing gesture promotes learning a mental transformation task better than seeing gesture. Developmental Science, 15(6), 876–884.Google Scholar

Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17(6), 722–738.Google Scholar

Hu, F. T., Ginns, P., & Bobis, J. (2015). Getting the point: Tracing worked examples enhances learning. Learning and Instruction, 35, 85–93.Google Scholar

Johnson-Glenberg, M. C., Birchfield, D. A., Tolentino, L., & Koziupa, T. (2014). Collaborative embodied learning in mixed reality motion-capture environments: Two science studies. Journal of Educational Psychology, 106(1), 86–104.Google Scholar

Kontra, C., Lyons, D. J., Fischer, S. M., & Beilock, S. L. (2015). Physical experience enhances science learning. Psychological Science, 26(6), 737–749.Google Scholar

Korbach, A., Ginns, P., Brünken, R., & Park, B. (2020). Should learners use their hands for learning? Results from an eye‐tracking study. Journal of Computer Assisted Learning, 36(1), 102–113.Google Scholar

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago, IL: University of Chicago Press.Google Scholar

Laski, E. V., & Siegler, R. S. (2014). Learning from number board games: You learn what you encode. Developmental Psychology, 50(3), 853–864.Google Scholar

Leopold, C., Mayer, R. E., & Dutke, S. (2019). The power of imagination and perspective in learning from science text. Journal of Educational Psychology, 111(5), 793–808.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

Macken, L., & Ginns, P. (2014). Pointing and tracing gestures may enhance anatomy and physiology learning. Medical Teacher, 36(7), 596–601.Google Scholar

Marley, S. C., Levin, J. R., & Glenberg, A. M. (2010). What cognitive benefits does an activity-based reading strategy afford young Native American readers? The Journal of Experimental Education, 78(3), 395–417.Google Scholar

Marley, S. C., & Szabo, Z. (2010). Improving children’s listening comprehension with a manipulation strategy. The Journal of Educational Research, 103(4), 227–238.Google Scholar

Marley, S. C., Szabo, Z., Levin, J. R., & Glenberg, A. M. (2011). Investigation of an activity-based text-processing strategy in mixed-age child dyads. The Journal of Experimental Education, 79(3), 340–360.Google Scholar

Martin, T., & Schwartz, D. L. (2005). Physically distributed learning: Adapting and reinterpreting physical environments in the development of fraction concepts. Cognitive Science, 29(4), 587–625.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., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology: Applied, 11(4), 256.Google Scholar

McNeil, N. M., & Fyfe, E. R. (2012). “Concreteness fading” promotes transfer of mathematical knowledge. Learning and Instruction, 22(6), 440–448.Google Scholar

McNeil, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on mathematics problems. Learning and Instruction, 19(2), 171–184.Google Scholar

Novack, M. A., Congdon, E. L., Hemani-Lopez, N., & Goldin-Meadow, S. (2014). From action to abstraction: Using the hands to learn math. Psychological Science, 25(4), 903–910.Google Scholar

Novack, M., & Goldin-Meadow, S. (2015). Learning from gesture: How our hands change our minds. Educational Psychology Review, 27(3), 405–412.Google Scholar

Olympiou, G., & Zacharia, Z. C. (2012). Blending physical and virtual manipulatives: An effort to improve students’ conceptual understanding through science laboratory experimentation. Science Education, 96(1), 21–47.Google Scholar

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

Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45.Google Scholar

Padalkar, S., & Hegarty, M. (2015). Models as feedback: Developing representational competence in chemistry. Journal of Educational Psychology, 107(2), 451–467.Google Scholar

Post, L. S., van Gog, T., Paas, F., & Zwaan, R. A. (2013). Effects of simultaneously observing and making gestures while studying grammar animations on cognitive load and learning. Computers in Human Behavior, 29(4), 1450–1455.Google Scholar

Pouw, W. T., van Gog, T., & Paas, F. (2014). An embedded and embodied cognition review of instructional manipulatives. Educational Psychology Review, 26(1), 51–72.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

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

Siegler, R. S., & Ramani, G. B. (2009). Playing linear number board games – but not circular ones – improves low-income preschoolers’ numerical understanding. Journal of Educational Psychology, 101(3), 545.Google Scholar

Singer, M. A., & Goldin-Meadow, S. (2005). Children learn when their teacher’s gestures and speech differ. Psychological Science, 16(2), 85–89.Google Scholar

Sommerville, J. A., Woodward, A. L., & Needham, A. (2005). Action experience alters 3-month-old infants’ perception of others’ actions. Cognition, 96(1), B1–B11.Google Scholar

Stull, A. T., Gainer, M. J., & Hegarty, M. (2018). Learning by enacting: The role of embodiment in chemistry education. Learning and Instruction, 55, 80–92.Google Scholar

Stull, A. T., & Hegarty, M. (2016). Model manipulation and learning: Fostering representational competence with virtual and concrete models. Journal of Educational Psychology, 108(4), 509–527.Google Scholar

Stull, A. T., Hegarty, M., Dixon, B., & Stieff, M. (2012). Representational translation with concrete models in organic chemistry. Cognition and Instruction, 30(4), 404–434.Google Scholar

Tang, M., Ginns, P., & Jacobson, M. J. (2019). Tracing enhances recall and transfer of knowledge of the water cycle. Educational Psychology Review, 31(2), 439–455.Google Scholar

Türkay, S. (2016). The effects of whiteboard animations on retention and subjective experiences when learning advanced physics topics. Computers & Education, 98, 102–114.Google Scholar

Uttal, D. H., Scudder, K. V., & DeLoache, J. S. (1997). Manipulatives as symbols: A new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology, 18(1), 37–54.Google Scholar

Valenzeno, L., Alibali, M. W., & Klatzky, R. (2003). Teachers’ gestures facilitate students’ learning: A lesson in symmetry. Contemporary Educational Psychology, 28(2), 187–204.Google Scholar

van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21(1), 21–30.Google Scholar

van Wermeskerken, M., Fijan, N., Eielts, C., & Pouw, W. T. (2016). Observation of depictive versus tracing gestures selectively aids verbal versus visual–spatial learning in primary school children. Applied Cognitive Psychology, 30(5), 806–814.Google Scholar

van Wermeskerken, M., & van Gog, T. (2017). Seeing the instructor’s face and gaze in demonstration video examples affects attention allocation but not learning. Computers & Education, 113, 98–107.Google Scholar

Wakefield, E. M., Congdon, E. L., Novack, M. A., Goldin-Meadow, S., & James, K. H. (2019). Learning math by hand: The neural effects of gesture-based instruction in 8-year-old children. Attention, Perception, & Psychophysics, 81(7), 2343–2353.Google Scholar

Willingham, D. T. (2017). Ask the cognitive scientist: Do manipulatives help students learn? American Educator, 6(2017), 7.Google Scholar

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

Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24(4), 345–376.Google Scholar

Wong, A., Marcus, N., Ayres, P., Smith, L., Cooper, G. A., Paas, F., & Sweller, J. (2009). Instructional animations can be superior to statics when learning human motor skills. Computers in Human Behavior, 25(2), 339–347.Google Scholar

Alhalabi, W. (2016). Virtual reality systems enhance students’ achievements in engineering education. Behaviour & Information Technology, 35(11), 919–925.Google Scholar

Baceviciute, S., Mottelson, A., Terkildsen, T., & Makransky, G. (2020). Investigating representation of text and audio in educational VR using learning outcomes and EEG. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20) (pp. 1–9). New York: ACM.Google Scholar

Baceviciute, S., Terkildsen, T. S., & Makransky, G. (in press). Investigating the Redundancy Principle in Immersive Virtual Reality Environments: An Eye-tracking and EEG Study. Journal of Computer Assisted Learning.Google Scholar

Bailenson, J. (2018). Experience on Demand: What Vrtual Reality Is, How it Works, and What it Can Do. New York: Norton & Company.Google Scholar

Barfield, W., Zeltzer, D., Sheridan, T. B., & Slater, M. (1995). Presence and performance within virtual environments. In Barfield, W., & Furness, T. A. (eds.), Virtual Environments and Advanced Interface Design (pp. 473–541). Oxford: Oxford University Press.Google Scholar

Checa, D., & Bustillo, A. (2020). A review of immersive virtual reality serious games to enhance learning and training. Multimedia Tools and Applications, 79(9), 5501–5527.Google Scholar

Chittaro, L., & Buttussi, F. (2015). Assessing knowledge retention of an immersive serious game vs. a traditional education method in aviation safety. IEEE Transactions on Visualization and Computer Graphics, 21(4), 529–538.Google Scholar

Cummings, J. J., & Bailenson, J. N. (2016). How immersive is enough? A meta-analysis of the effect of immersive technology on user presence. Media Psychology, 19(2), 272–309.Google Scholar

Dalgarno, B., & Lee, M. J. W. (2010). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1), 10–32.Google Scholar

Ferguson, C., van den Broek, E. L., & van Oostendorp, H. (2020). On the role of interaction mode and story structure in virtual reality serious games. Computers & Education, 143, 1–10.Google Scholar

Jensen, L., & Konradsen, F. (2018). A review of the use of virtual reality head-mounted displays in education and training. Education and Information Technologies, 23(4), 1515–1529.Google Scholar

Johnson-Glenberg, M. C. (2019). The necessary nine: Design principles for embodied VR and active stem education. In Díaz, P., Ioannou, A., Bhagat, K., & Spector, J. (eds.), Learning in a Digital World. Smart Computing and Intelligence (pp. 83–112). Singapore: Springer.Google Scholar

Klingenberg, S., Jørgensen, M. L., Dandanell, G., Skriver, K., Mottelson, A., & Makransky, G. (2020). Investigating the effect of teaching as a general learning strategy when learning through desktop and immersive VR: A media and methods experiment. British Journal of Educational Technology, 51(6), 2115–2138.Google Scholar

Leahy, W., & Sweller, J. (2011). Cognitive load theory, modality of presentation and the transient information effect. Applied Cognitive Psychology, 25(6), 943–951.Google Scholar

Lee, E. A.-L., Wong, K. W., & Fung, C. C. (2010). How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach. Computers and Education, 55(4), 1424–1442.Google Scholar

Makransky, G., Andreasen, N. K, Baceviciute, S., & Mayer, R. M. (2020). Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality. Journal of Educational Psychology. DOI: 10.1037/edu0000473.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

Makransky, G., & Lilleholt, L. (2018). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educational Technology Research and Development, 66, 1141–1164.Google Scholar

Makransky, G., Mayer, R., Nøremølle, A., Cordoba, A. L., Wandall, J., & Bonde, M. (2020). Investigating the feasibility of using assessment and explanatory feedback in desktop virtual reality simulations. Educational Technology Research and Development, 68(1), 293–317.Google Scholar

Makransky, G., & Petersen, G. B. (2019). Investigating the process of learning with desktop virtual reality: A structural equation modeling approach. Computers & Education, 134, 15–30.Google Scholar

Makransky, G., & Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, DOI: 10.1007/s10648-020-09586-2.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.Google Scholar

Makransky, G., Wismer, P., & Mayer, R. E. (2019). A gender matching effect in learning with pedagogical agents in an immersive virtual reality science simulation. Journal of Computer Assisted Learning, 35(3), 349–358.Google Scholar

Marsh, T., & Smith, S. P. (2001). Guiding user navigation in virtual environments using awareness of virtual off-screen space. In Proceedings of the Workshop on Guiding Users through Interactive Experiences – Usability Centred Design and Evaluation of Virtual 3D Environments, 149–154.Google Scholar

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

Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment. Computers & Education, 140, 1–17.Google Scholar

Mikropoulos, T. A., & Natsis, A. (2011). Educational virtual environments: A ten- year review of empirical research (1999–2009). Computers and Education, 56(3), 769–780.Google Scholar

Moreno, R., & Mayer, R. E. (2002). Learning science in virtual reality multimedia environments: Role of methods and media. Journal of Educational Psychology, 94(3), 598–610.Google Scholar

Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96(1), 165–173.Google Scholar

Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of Educational Psychology, 110, 785–797.Google Scholar

Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61.Google Scholar

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341.Google Scholar

Petersen, G. B., Klingenberg, S., Mayer, R. E., & Makransky, G. (2020). The virtual field trip: Investigating how to optimize immersive virtual learning in climate change education. British Journal of Educational Technology, 51(6), 2099–2115.Google Scholar

Rothe, S., & Hußmann, H. (2018). Guiding the viewer in cinematic virtual reality by diegetic cues. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics (pp. 101–117). Cham: Springer.Google Scholar

Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence: Teleoperators & Virtual Environments, 6(6), 603–616.Google Scholar

Snelson, C., & Hsu, Y. C. (2020). Educational 360-degree videos in virtual reality: A scoping review of the emerging research. TechTrends, 64, 404–412.Google Scholar

Terkildsen, T. S., & Makransky, G. (2019). Measuring presence in video games: An investigation of the potential use of physiological measures as indicators of presence. International Journal of Human Computer Studies, 126, 64–80.Google Scholar

van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.Google Scholar

Webster, R. (2016). Declarative knowledge acquisition in immersive virtual learning environments. Interactive Learning Environments, 24(6), 1319–1333.Google Scholar

Wu, B.., Yu, X., & Gu, X. (2020). Effectiveness of immersive virtual reality using head-mounted displays on learning performance: A meta-analysis. British Journal of Educational Technology, 51(6), 1991–2005.Google Scholar

Aronson, E., Blaney, N., Stephan, C., Sikes, J., & Snapp, M. (1978). The Jigsaw Classroom. Beverly Hills, CA: Sage.Google Scholar

Asterhan, C. S. C., & Schwarz, B. B. (2016). Argumentation for learning: Well-trodden paths and unexplored territories. Educational Psychologist, 51(2), 164–187.Google Scholar

Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70(2), 181–214.Google Scholar

Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.Google Scholar

Buchs, C., & Butera, F. (2009). Is a partner’s competence threatening during dyadic cooperative work? It depends on resource interdependence. European Journal of Psychology of Education, 24, 145–154.Google Scholar

Chen, J., Wang, M., Kirschner, P. A., & Tsai, C.-C. (2018). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799–843.Google Scholar

Ciborra, C., & Olson, M. H. (1988). Encountering electronic work groups: A transaction costs perspective. In Proceedings of the 1988 ACM Conference on Computer-Supported Cooperative Work (pp. 94–101). Portland, OR: ACM.Google Scholar

Ellis, C. A., Gibbs, S. J., & Rein, G. (1992). Groupware: Some issues and experiences. In Marca, D., & Bock, G. (eds.), Groupware: Software for Computer-Supported Cooperative Work (pp. 23–43). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar

Erkens, M., & Bodemer, D. (2019). Improving collaborative learning: Guiding knowledge exchange through the provision of information about learning partners and learning contents. Computers & Education, 128, 452–472.Google Scholar

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

Fitzsimons, G. M., Finkel, E. J., & vanDellen, M. R. (2015). Transactive goal dynamics. Psychological Review, 122(4), 648–673.Google Scholar

Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997). The emerging conceptualization of groups as information processors. Psychological Bulletin, 121(1), 43–64.Google Scholar

Hollingshead, A. B. (2001). Cognitive interdependence and convergent expectations in transactive memory. Journal of Personality and Social Psychology, 81(6), 1080–1089.Google Scholar

Janssen, J., Erkens, G., Kanselaar, G., & Jaspers, J. (2007). Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Computers & Education, 49(4), 1037–1065.Google Scholar

Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7, 25–43.Google Scholar

Janssen, J., Kirschner, F., Erkens, G., Kirschner, P. A., & Paas, F. (2010). Making the black box of collaborative learning transparent: Combining process-oriented and cognitive load approaches. Educational Psychology Review, 22, 139–154.Google Scholar

Janssen, J., & Kirschner, P. A. (2020). Applying collaborative cognitive load theory to computer-supported collaborative learning: Towards a research agenda. Educational Technology Research and Development, 68, 783–805.Google Scholar

Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research and Development, 63, 125–142.Google Scholar

Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38, 365–379.Google Scholar

Johnson, D. W., & Johnson, R. T. (2014). Using technology to revolutionize cooperative learning: An opinion. Frontiers in Psychology, 5, 1156.Google Scholar

Johnson, D. W., Johnson, R. T., & Stanne, M. B. (1989). Impact of goal and resource interdependence on problem-solving success. The Journal of Social Psychology, 129(5), 621–629.Google Scholar

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

Kirschner, F., Paas, F., & Kirschner, P. A. (2009a). A cognitive-load approach to collaborative learning: United brains for complex tasks. Educational Psychology Review, 21, 31–42.Google Scholar

Kirschner, F., Paas, F., & Kirschner, P. A. (2009b). Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency. Computers in Human Behavior, 25(2), 306–314.Google Scholar

Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624.Google Scholar

Kirschner, F., Paas, F., Kirschner, P. A., & Janssen, J. (2011). Differential effects of problem-solving demands on individual and collaborative learning outcomes. Learning and Instruction, 21(4), 587–599.Google Scholar

Kirschner, P. A., & Erkens, G. (2013). Toward a framework for CSCL research. Educational Psychologist, 48(1), 1–8.Google Scholar

Kirschner, P. A., Sweller, J., & Clark, R. E. (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(2), 75–86.Google Scholar

Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, R. J. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13, 213–233.Google Scholar

Le, H., Janssen, J., & Wubbels, T. (2018). Collaborative learning practices: Teacher and student perceived obstacles to effective student collaboration. Cambridge Journal of Education, 48, 103–122.Google Scholar

Lou, Y., Abrami, P., Spence, J., Poulsen, C., Chambers, B., & d’Apollonia, S. (1996). Within-class grouping: A meta-analysis. Review of Educational Research, 66, 423–458.Google Scholar

Malone, T. W., & Crowston, K. (1992). What is coordination theory and how can it help design cooperative work systems? In Marca, D., & Bock, G. (eds.), Groupware: Software for Computer-Supported Cooperative Work (pp. 100–113). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar

Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1), 14–19.Google Scholar

Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63(8), 760–769.Google Scholar

Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423.Google Scholar

Nokes-Malach, T. J., Zepeda, C. D., Richey, E., & Gadgil, S. (2019). Collaborative learning: The benefits and costs. In Dunlosky, J., & Rawson, K. A. (eds.), The Cambridge Handbook of Cognition and Learning (pp. 500–527). Cambridge: Cambridge University Press.Google Scholar

Noroozi, O., Teasley, S. D., Biemans, H. J. A., Weinberger, A., & Mulder, M. (2013). Facilitating learning in multidisciplinary groups with transactive CSCL scripts. International Journal of Computer-Supported Collaborative Learning, 8, 189–223.Google Scholar

Peterson, A. T., & Roseth, C. J. (2016). Effects of four CSCL strategies for enhancing online discussion forums: Social interdependence, summarizing, scripts, and synchronicity. International Journal of Educational Research, 76, 147–161.Google Scholar

Popov, V., van Leeuwen, A., & Buijs, S. C. A. (2017). Are you with me or not? Temporal synchronicity and transactivity during CSCL. Journal of Computer Assisted Learning, 33(5), 424–442.Google Scholar

Retnowati, E., Ayres, P., & Sweller, J. (2017). Can collaborative learning improve the effectiveness of worked examples in learning mathematics? Journal of Educational Psychology, 109(5), 666–679.Google Scholar

Roseth, C. J., Johnson, D. W., & Johnson, R. T. (2008). Promoting early adolescents’ achievement and peer relationships: The effects of cooperative, competitive, and individualistic goal structures. Psychological Bulletin, 134, 223–246.Google Scholar

Roseth, C. J., Lee, Y. K., & Saltarelli, W. A. (2019). Reconsidering Jigsaw social psychology: Longitudinal effects on social interdependence, sociocognitive conflict regulation, motivation, and achievement. Journal of Educational Psychology, 111(1), 149–169.Google Scholar

Slof, B., van Leeuwen, A., Janssen, J., & Kirschner, P. A. (2021). Mine, ours, and yours: Whose engagement and prior knowledge affects individual achievement from online collaborative learning? Journal of Computer Assisted Learning, 37, 39–50.Google Scholar

Stodolsky, S. S. (1984). Frameworks for studying instructional processes in peer work-groups. In Peterson, P. L., Wilkinson, L. C., & Hallinan, M. (eds.), The Social Context of Instruction: Group Organization and Group Processes (pp. 107–124). Orlando, FL: Academic Press.Google Scholar

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

Sweller, J. (2010). Element interactivity and intrinsic, extraneous and germane cognitive load. Educational Psychology Review, 22, 123–138.Google Scholar

Teasley, S. D., & Roschelle, J. (1993). Constructing a joint problem space: The computer as a tool for sharing knowledge. In Lajoie, S. P. (ed.), Computers as Cognitive Tools: Technology in Education (pp. 229–258). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar

Tindale, R. S., & Kameda, T. (2000). Social sharedness as a unifying theme for information processing in groups. Group Processes & Intergroup Relations, 3, 123–140.Google Scholar

van den Bossche, P., Gijselaers, W., Segers, M., Woltjer, G., & Kirschner, P. (2011). Team learning: Building shared mental models. Instructional Science, 39, 283–301.Google Scholar

Webb, N. M. (2013). Information processing approaches to collaborative learning. In Hmelo-Silver, C. E., Chinn, C. A., Chan, C. K. K., & O’Donnell, A. M. (eds.), The International Handbook of Collaborative Learning (pp. 19–40). New York: Routledge.Google Scholar

Webb, N. M., & Farivar, S. (1994). Promoting helping-behavior in cooperative small-groups in middle school mathematics. American Educational Research Journal, 31(2), 369–395.Google Scholar

Webb, N. M., & Farivar, S. (1999). Developing productive group interaction in middle school mathematics. In O’Donnell, A., & King, A. (eds.), Cognitive Perspectives on Peer Learning (pp. 117–149). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Webb, N. M., & Mastergeorge, A. (2003). Promoting effective helping behavior in peer-directed groups. International Journal of Educational Research, 39, 73–97.Google Scholar

Webb, N. M., Troper, J., & Fall, R. (1995). Constructive activity and learning in collaborative small-groups. Journal of Educational Psychology, 87(3), 406–423.Google Scholar

Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In Mullen, B., & Goethals, G. R. (eds.), Theories of Group Behavior (pp. 185–208). Berlin: Springer-Verlag.Google Scholar

Wegner, D. M. (1995). A computer network model of human transactive memory. Social Cognition, 13(3), 319–339.Google Scholar

Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24(4), 345–376.Google Scholar

Yamane, D. (1996). Collaboration and its discontents: Steps toward overcoming barriers to successful group projects. Teaching Sociology, 24(4), 378–383.Google Scholar

Zambrano, J. R., Kirschner, F., Sweller, J., & Kirschner, P. A. (2019a). Effects of group experience and information distribution on collaborative learning. Instructional Science, 47(5), 531–550.Google Scholar

Zambrano, J. R., Kirschner, F., Sweller, J., & Kirschner, P. A. (2019b). Effects of prior knowledge on collaborative and individual learning. Learning and Instruction, 63, 101214.Google Scholar

Zhang, L., Kalyuga, S., Lee, C., Lei, C., & Jiao, J. (2015). Effectiveness of collaborative learning with complex tasks under different learning group formations: A cognitive load perspective. In Cheung, S., Kwok, L., Yang, H., Fong, J., & Kwan, R. (eds.), Hybrid Learning: Innovation in Educational Practices (pp. 149–159). Berlin: Springer.Google Scholar

Alpizar, D., Adesope, O. O., & Wong, R.M. (2020). A meta-analysis of signaling principle in multimedia learning environments. Education Technology Research and Development, 68, 2095–2119.Google Scholar

Berney, S., & Bétrancourt, M. (2016). Does animation enhance learning? A meta-analysis. Computers & Education, 101, 150-167.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

Boucheix, J.-M., & Lowe, R. K. (2010). An eye tracking comparison of external pointing cues and internal continuous cues in learning with complex animations. Learning and Instruction, 20, 123–135.Google Scholar

Boucheix, J.-M., Lowe, R. K., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71–84.Google Scholar

Castro-Alonzo, J. C., Wong, M., Adesope, O. O., & Ayres, P. (2019). Gender imbalance in instructional dynamic versus static visualizations: A meta-analysis. Educational Psychology Review, 31, 361–387.Google Scholar

de Koning, B. B., & Tabbers, H. K. (2013). Gestures in instructional animations: A helping hand to understanding non-human movements? Applied Cognitive Psychology, 1(27), 683–689.Google Scholar

de Koning, B. B., Tabbers, H. K., Rijkers, R. M. P. J., & Paas, F. (2010a). Attention cueing in an instructional animation: The role of presentation speed. Computers in Human Behavior, 27, 41–45.Google Scholar

de Koning, B. B., Tabbers, H. K., Rijkers, R. M. J. P., & Paas, F. (2010b). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20, 111–122.Google Scholar

de Koning, B. B., Tabbers, H. K., Rijkers, R. M. J. P., & Paas, F. (2011). Improved effectiveness of cueing by self‐explanations when learning from a complex animation. Applied Cognitive Psychology, 25, 183–194.Google Scholar

Fischer, S., Lowe, R. K., & Schwan, S. (2008). Effects of presentation speed of a dynamic visualization on the understanding of a mechanical system. Applied Cognitive Psychology, 22, 1126–1141.Google Scholar

Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738.Google Scholar

Lowe, R. K., & Boucheix, J.-M. (2008). Learning from animated diagrams: How are mental models built? In Stapleton, G., Howse, J., & Lee, J. (eds.), Diagrammatic Representation and Inference (pp. 266–281). Berlin: Springer.Google Scholar

Lowe, R. K., & Boucheix, J.-M. (2010). Manipulatable models for investigating processing of dynamic diagrams. In Goel, A. K., Jamnik, M., & Narayanan, N. H. (eds.), Diagrammatic Representation and Inference (pp. 319–321). Berlin: Springer.Google Scholar

Lowe, R. K., & Boucheix, J.-M. (2011). Cueing complex animation: Does direction of attention foster learning processes? Learning and Instruction, 21, 650–663.Google Scholar

Lowe, R. K., & Boucheix, J.-M. (2012a). Dynamic diagrams: A composition alternative. In Cox, P., Plimmer, B., & Rogers, P. (eds.), Diagrammatic Representation and Inference (pp. 233–240). Berlin: Springer.Google Scholar

Lowe, R. K., & Boucheix, J-M. (2012b). Addressing challenges of biological animations. In de Vries, E., & Scheiter, K. (eds.), Proceedings of the Meeting of the EARLI Special Interest Group on Comprehension of Text and Graphics (pp. 217–129). Grenoble: University of Grenoble.Google Scholar

Lowe, R. K., & Boucheix, J. M. (2016). Principled animation design improves comprehension of complex dynamics. Learning and Instruction, 45, 72–84.Google Scholar

Lowe, R.K., & Boucheix, J-M. (2020). Improving animations: Compositional anti-cueing makes conventional designs more effective. Paper presented at EARLI SIG2, Comprehension of Text and Graphics. Charles University, Prague, Czech Republic. Online Conference, August 31–September 2.Google Scholar

Lowe, R. K., Boucheix, J. M., & Menant, M. (2018). Perceptual processing and the comprehension of relational information in dynamic diagrams. In Chapman, P., Stapleton, G., Moktefi, A., Perez-Kriz, S., & Bellucci, F. (eds.), Diagrammatic Representation and Inference, LNAI, Lecture Notes in Artificial Intelligence, 10871 (pp. 470–483). Cham: Springer.Google Scholar

Meyer, K., Rasch, T., & Schnotz, W. (2010). Effects of animation’s speed of presentation on perceptual processing and learning. Learning and Instruction, 20, 136–145.Google Scholar

Mierowsky, R., Marcus, N., & Ayres, P. (2019). Using mimicking gestures to improve observational learning from instructional videos. Educational Psychology, 40, 1–20.Google Scholar

Neisser, U. (1976). Cognition and Reality. San Francisco, CA: Freeman.Google Scholar

Ploetzner, R., Berney, S., & Bétrancourt, M. (2020). A review of learning demands in instructional animations: The educational effectiveness of animations unfolds if the features of change need to be learned. Journal of Computer Assisted Learning, 36(6), 838–860.Google Scholar

Ploetzner, R., & Fillisch, B. (2017). Not the silver bullet: Learner-generated drawings make it difficult to understand broader spatiotemporal structures in complex animations. Learning and Instruction, 47, 13–24.Google Scholar

Post, L. S., van Gog, T., Paas, F., & Zwaan, R. A. (2013). Effects of simultaneously observing and making gestures while studying grammar animations on cognitive load and learning. Computers in Human Behavior, 29, 1450–1455.Google Scholar

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

Richter, J., Scheiter, K., & Eitel, A. (2016). Signaling text-picture relations in multimedia learning: A comprehensive meta-analysis. Educational Research Review, 17, 19–36.Google Scholar

Scheiter, K. (2014). The learner control principle in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 487–512). New York: Cambridge University Press.Google Scholar

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

Schwan, S., & Riempp, R. (2004). The cognitive benefits of interactive videos: Learning to tie nautical knots. Learning and Instruction, 14, 293–305.Google Scholar

Sepp, S., Agostinho, S., Tindall-Ford, S., & Paas, F (in press). To trace or not to trace? Meaningful gesture for learning geometry using touch-based multimedia learning materials.Google Scholar

Spanjers, I. A. E., van Gog, T., & van Merrienboer, J. J. G. (2010). A theoretical analysis of how segmentation of dynamic visualizations optimizes students’ learning. Educational Psychology Review, 22, 411–423.Google Scholar

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

Stull, A. T., Gainer, M. J., & Hegarty, M. (2018). Learning by enacting: The role of embodiment in chemistry education. Learning and Instruction, 55, 80–92.Google Scholar

Ullman, S. (1984). Visual routines. Cognition, 18, 97–159.Google Scholar

van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21, 21–30.Google Scholar

van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17, 285–325.Google Scholar

Wong, A., Leahy, W., Marcus, N., & Sweller, J. (2012). Cognitive load theory, the transient information effect and e-learning. Learning and Instruction, 22, 449–457.Google Scholar

Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological Bulletin, 127, 3–21.Google Scholar

Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–561.Google Scholar

Astleitner, H., & Leutner, D. (2014). Designing instructional technology from an emotional perspective. Journal of Research on Computing in Education, 32, 497–510.Google Scholar

Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human–Computer Studies, 68(4), 223–241.Google Scholar

Beilock, S. L., Kulp, C. A., Holt, L. E., & Carr, T. H. (2004). More on the fragility of performance: Choking under pressure in mathematical problem solving. Journal of Experimental Psychology: General, 133(4), 584–600.Google Scholar

Bianchi-Berthouze, N., Kim, W. W., & Patel, D. (2007). Does body movement engage you more in digital game play? and why? In International Conference on Affective Computing and Intelligent Interaction (pp. 102–113). Berlin: Springer.Google Scholar

Biles, M. L., Plass, J., & Homer, B. D. (2018). Designing digital badges for educational games: The impact of badge type on student motivation and learning. International Journal of Gaming and Computer-Mediated Simulations, 10(4), 1–19.Google Scholar

Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59.Google Scholar

Bradley, M. M., & Lang, P. J. (2000). Affective reactions to acoustic stimuli. Psychophysiology, 37(2), 204–215.Google Scholar

Brom, C., Děchtěrenko, F., Frollová, N., Stárková, T., Bromová, E., & D’Mello, S. K. (2017). Enjoyment or involvement? Affective-motivational mediation during learning from a complex computerized simulation. Computers & Education, 114, 236–254.Google Scholar

Brom, C., Starkova, T., & D’Mello, S. K. (2018). How effective is emotional design? A meta-analysis on facial anthropomorphisms and pleasant colors during multimedia learning. Educational Research Review, 25, 100–119.Google Scholar

Chung, S., & Cheon, J. (2020). Emotional design of multimedia learning using background images with motivational cues. Journal of Computer Assisted Learning, 36(6), 922–932.Google Scholar

Chung, S., Cheon, J., & Lee, K. W. (2015). Emotion and multimedia learning: An investigation of the effects of valence and arousal on different modalities in an instructional animation. Instructional Science, 43(5), 545–559.Google Scholar

Derryberry, D., & Tucker, D. M. (1994). Motivating the focus of attention. In Niedenthal, P. M., & Kitayama, S. (eds.), The Heart’s Eye: Emotional Influences in Perception and Attention (pp. 167–196). San Diego, CA: Academic Press.Google Scholar

D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25(7), 1299–1308.Google Scholar

D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.Google Scholar

D’Mello, S., & Graesser, A. (2013). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4), 1–39.Google Scholar

D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.Google Scholar

Eerola, T., Friberg, A., & Bresin, R. (2013). Emotional expression in music: Contribution, linearity, and additivity of primary musical cues. Frontiers in Psychology, 4, 487.Google Scholar

Endres, T., Weyreter, S., Renkl, A., & Eitel, A. (2020). When and why does emotional design foster learning? Evidence for situational interest as a mediator of increased persistence. Journal of Computer Assisted Learning, 36(4), 514–525.Google Scholar

Fraser, K., Ma, I., Teteris, E., Baxter, H., Wright, B., & McLaughlin, K. (2012). Emotion, cognitive load and learning outcomes during simulation training. Medical Education, 46(11), 1055–1062.Google Scholar

Gatti, E., Calzolari, E., Maggioni, E., & Obrist, M. (2018). Emotional ratings and skin conductance response to visual, auditory and haptic stimuli. Scientific Data, 5, 180120.Google Scholar

Graesser, A. C. (2019). Emotions are the experiential glue of learning environments in the 21st century. Learning and Instruction, 70, 101212.Google Scholar

Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material. In Ross, B. H. (ed.), Psychology of Learning and Motivation (Vol. 57, pp. 183–225). Cambridge, MA: Academic Press.Google Scholar

Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90(3), 414.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

Heidig, S., Müller, J., & Reichelt, M. (2015). Emotional design in multimedia learning: Differentiation on relevant design features and their effects on emotions and learning. Computers in Human Behavior, 44, 81–95.Google Scholar

Homer, B. D., Plass, J. L., Rose, M. C., MacNamara, A., Pawar, S., & Ober, T. M. (2019). Activating adolescents’ “hot” executive functions in a digital game to train cognitive skills: The effects of age and prior abilities. Cognitive Development, 49, 20–32.Google Scholar

Isbister, K. (2016). How Games Move Us: Emotion by Design. Cambridge, MA: MIT Press.Google Scholar

Isen, A. M., Daubman, K. A., & Nowicki, G. P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52(6), 1122.Google Scholar

Isen, A. M., Shalker, T. E., Clark, M., & Karp, L. (1978). Affect, accessibility of material in memory, and behavior: A cognitive loop? Journal of Personality and Social Psychology, 36(1), 1–12.Google Scholar

Izard, C. E. (2007). Basic emotions, natural kinds, emotion schemas, and a new paradigm. Perspectives on Psychological Science, 2, 260–280.Google Scholar

Izard, C. E. (2009). Emotion theory and research: Highlights, unanswered questions, and emerging issues. Annual Review of Psychology, 60, 1–25.Google Scholar

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

Knörzer, L., Brünken, R., & Park, B. (2016a). Facilitators or suppressors: Effects of experimentally induced emotions on multimedia learning. Learning and Instruction, 44, 97–107.Google Scholar

Knörzer, L., Brünken, R., & Park, B. (2016b). Emotions and multimedia learning: The moderating role of learner characteristics. Journal of Computer Assisted Learning, 32(6), 618–631.Google Scholar

Kühl, T., Moersdorf, F., Römer, M., & Münzer, S. (2019). Adding emotionality to seductive details – Consequences for learning? Applied Cognitive Psychology, 33(1), 48–61.Google Scholar

Laird, J. D., Wagener, J. J., Halal, M., & Szegda, M. (1982). Remembering what you feel: Effects of emotion on memory. Journal of Personality and Social Psychology, 42(4), 646.Google Scholar

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97(3), 377.Google Scholar

Le, Y., Liu, J., Deng, C., & Dai, D. Y. (2018). Heart rate variability reflects the effects of emotional design principle on mental effort in multimedia learning. Computers in Human Behavior, 89, 40–47.Google Scholar

Lewis, M. D. (1995). Cognition-emotion feedback and the self-organization of developmental paths. Human Development, 38, 71–102.Google Scholar

Li, J., Luo, C., Zhang, Q., & Shadiev, R. (2020). Can emotional design really evoke emotion in multimedia learning? International Journal of Educational Technology in Higher Education, 17, 1–18.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

Loderer, K., Pekrun, R., & Plass, J.L. (2020). Emotional foundations of game-based learning. In Plass, J. L., Mayer, R. E., & Homer, B. D. (eds.), Handbook of Game-based Learning (pp. 111–151). Cambridge, MA: MIT Press.Google Scholar

Lorenz, K. (1950). Ganzheit und Teil in der tierischen und menschlichen Gemeinschaft [Part and Parcel in Animal and Human Societies]. Studium Generale, 3(9), 455–499.Google Scholar

Magner, U. I., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning and instruction, 29, 141–152.Google Scholar

Mayer, R. E. (2005). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (p. 31–48). New York: Cambridge University Press.Google 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., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12–18.Google Scholar

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.Google Scholar

Münchow, H., & Bannert, M. (2019). Feeling good, learning better? Effectivity of an emotional design procedure in multimedia learning. Educational Psychology, 39(4), 530–549.Google Scholar

Münchow, H., Mengelkamp, C., & Bannert, M. (2017). The better you feel the better you learn: Do warm colours and rounded shapes enhance learning outcome in multimedia learning? Education Research International, 2017(2), 1–15.Google Scholar

Navratil, S. D., Kühl, T., & Heidig, S. (2018). Why the cells look like that – the influence of learning with emotional design and elaborative interrogations. Frontiers in Psychology, 9, 1653.Google Scholar

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

Paivio, A. (2013). Dual coding theory, word abstractness, and emotion: A critical review of Kousta et al. (2011). Journal of Experimental Psychology: General, 142(1), 282–287.Google Scholar

Park, B., Flowerday, T., & Brünken, R. (2015). Cognitive and affective effects of seductive details in multimedia learning. Computers in Human Behavior, 44, 267–278.Google Scholar

Park, B., Knörzer, L., Plass, J. L., & Brünken, R. (2015). Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Computers & Education, 86, 30–42.Google Scholar

Pawar, S., Tam, F., & Plass, J. L. (2020). Emerging design factors in game-Bbased learning: Emotional design, musical score, and game mechanics design. In Plass, J. L., Mayer, R. E., & Homer, B. D. (eds.), Handbook of Game-based Learning (pp. 347–365). Cambridge, MA: MIT Press.Google Scholar

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.Google Scholar

Pekrun, R., & Linnenbrink-Garcia, L. (eds.) (2014). International Handbook of Emotions in Education. New York: Routledge.Google Scholar

Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9(2), 148–158.Google Scholar

Phelps, E. A. (2004). Human emotion and memory: Interactions of the amygdala and hippocampal complex. Current Opinion in Neurobiology, 14(2), 198–202.Google Scholar

Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128–140.Google Scholar

Plass, J. L., Homer, B. D., Hayward, E. O., Frye, J., Huang, T. T., Biles, M., Stein, M., & Perlin, K. (2012). The effect of learning mechanics design on learning outcomes in a computer-based geometry game. E-Learning and Games for Training, Education, Health and Sports. Lecture Notes in Computer Science, 7516, 65–71.Google Scholar

Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258–283.Google Scholar

Plass, J. L., Homer, B. D., MacNamara, A., Ober, T., Rose, M. C., Hovey, C. M., Pawar, S., & Olsen, A. (2019). Emotional design for digital games for learning: The affective quality of expression, color, shape, and dimensionality of game characters. Learning and Instruction, 70, 101194.Google Scholar

Plass, J. L., Homer, B. D., Pawar, S., & Tam, F. (2018). Connecting theory and design through research: Cognitive skills training games. In Göbel, S., Garcia-Agundez, A., Tregel, T., Ma, M., Hauge, J. B., Oliveira, M., Marsh, T., & Caserman, P. (eds.), Serious Games. JCSG 2018. Lecture Notes in Computer Science (vol 11243). Cham: Springer.Google Scholar

Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31, 339–359.Google Scholar

Plass, J. L., & Kaplan, U. (2016). Emotional design in digital media for learning. In Tettegah, S., & Gartmeier, M. (eds.), Emotions, Technology, Design, and Learning (pp. 131–161). New York: Elsevier.Google Scholar

Plass, J. L., Mayer, R. E., & Homer, B. D. (eds.) (2020). Handbook of Game-Based Learning. Cambridge, MA: MIT Press.Google Scholar