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4 - Research Methods in Multimedia Learning

from Part I - Background

Published online by Cambridge University Press:  19 November 2021

By
Halszka Jarodzka
Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
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Summary

This chapter describes diverse research methods to study multimedia learning. In light of the wide range of methods to study learning with multimedia and to stay in line with the focus of this Handbook, I target experimental research where a variation of multimedia design is tested against (at least) a control design. Thus, I omit case studies, technical developments, design-based research, etc. Moreover, I only take into consideration research in which the main dependent measure was some sort of learning outcome, such as performance, retention, or transfer. In addition, I look into variables mediating the way to this learning outcome. In this way I come to the following structuring of measures: tests that a priori capture characteristics of learners, measures that online trace the process of learning, self-reports of how learners experienced this learning, and learning outcome measures. For each type of measure, I provide a description and concrete examples of their use in multimedia research. Lastly, I explore thus far, less-frequently used methods in multimedia research, that have, however, the potential to shed new light on multimedia learning.

Keywords

research methodseye-trackinglearning outcome measuresself-report measuresonline trace measuresverbal reports
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 41 - 54
Publisher: Cambridge University Press
Print publication year: 2021

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References

Ahern, S., & Beatty, J. (1979). Pupillary responses during information processing vary with scholastic aptitude test scores. Science, 205(4412), 1289–1292.CrossRefGoogle ScholarPubMed
Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428.Google Scholar
Amadieu, F., van Gog, T., Paas, F., Tricot, A., & Mariné, C. (2009). Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning. Learning and Instruction, 19(5), 376–386.Google Scholar
Anmarkrud, Ø., Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61–83.CrossRefGoogle Scholar
Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.CrossRefGoogle Scholar
Argelagós, E., Brand-Gruwel, S., Jarodzka, H., & Pifarré, M. (2018). Unpacking cognitive skills engaged in web-search: How can log files, eye movements, and cued-retrospective reports help? An in-depth qualitative case study. International Journal of Innovation and Learning, 24(2), 152–175.CrossRefGoogle Scholar
Arslan-Ari, I., Crooks, S. M., & Ari, F. (2020). How much cueing is needed in instructional animations? The role of prior knowledge. Journal of Science Education and Technology, 29(5), 666–676.Google Scholar
Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389–400.CrossRefGoogle Scholar
Baars, M., van Gog, T., de Bruin, A., & Paas, F. (2018). Accuracy of primary school children’s immediate and delayed judgments of learning about problem-solving tasks. Studies in Educational Evaluation, 58, 51–59.Google Scholar
Baars, M., Wijnia, L., de Bruin, A., & Paas, F. (2020). The relation between student’s effort and monitoring judgments during learning: A meta-analysis. Educational Psychology Review, 32, 979–1002.Google Scholar
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1–29.Google Scholar
Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185.CrossRefGoogle Scholar
Beege, M., Ninaus, M., Schneider, S., Nebel, S., Schlemmel, J., Weidenmüller, J., Moeller, K., & Rey, G. D. (2020). Investigating the effects of beat and deictic gestures of a lecturer in educational videos. Computers & Education, 156, 103955.CrossRefGoogle Scholar
Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A., & Montanari, R. (2011). Driver workload and eye blink duration. Transportation Research Part F: Traffic Psychology and Behaviour, 14(3), 199–208.Google Scholar
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student–teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401–411.CrossRefGoogle ScholarPubMed
Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, 411–417.Google Scholar
Blayney, P., Kalyuga, S., & Sweller, J. (2016). The impact of complexity on the expertise reversal effect: Experimental evidence from testing accounting students. Educational Psychology, 36(10), 1868–1885.CrossRefGoogle Scholar
Bokosmaty, S., Sweller, J., & Kalyuga, S. (2015). Learning geometry problem solving by studying worked examples: Effects of learner guidance and expertise. American Educational Research Journal, 52(2), 307–333.Google Scholar
Boucheix, J.-M., Gauthier, P., Fontaine, J.-B., & Jaffeux, S. (2018). Mixed camera viewpoints improve learning medical hand procedure from video in nurse training? Computers in Human Behavior, 89, 418–429.Google Scholar
Brand-Gruwel, S., Kammerer, Y., van Meeuwen, L., & van Gog, T. (2017). Source evaluation of domain experts and novices during Web search: Evaluation of sources. Journal of Computer Assisted Learning, 33(3), 234–251.CrossRefGoogle Scholar
Brucker, B., Ehlis, A.-C., Häußinger, F. B., Fallgatter, A. J., & Gerjets, P. (2015). Watching corresponding gestures facilitates learning with animations by activating human mirror-neurons: An fNIRS study. Learning and Instruction, 36, 27–37.CrossRefGoogle Scholar
Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor-Analytic Studies. Cambridge: Cambridge University Press.Google Scholar
Chan, K. Y., Lyons, C., Kon, L. L., Stine, K., Manley, M., & Crossley, A. (2020). Effect of on-screen text on multimedia learning with native and foreign-accented narration. Learning and Instruction, 67, 101305.Google Scholar
Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Thousand Oaks, CA: Sage Publication.Google Scholar
Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide, The Journal of the Learning Sciences, 6(3), 271–315.Google Scholar
Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar
Chisari, L. B., Mockevičiūtė, A., Ruitenburg, S. K., Vemde, L., Kok, E. M., & Gog, T. (2020). Effects of prior knowledge and joint attention on learning from eye movement modelling examples. Journal of Computer Assisted Learning, 36(4), 569–579.Google Scholar
Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324.CrossRefGoogle Scholar
Corsi, P. M. (1972). Human Memory and the Medial Temporal Region of the Brain. Montreal, QC: McGill University.Google Scholar
Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26(2), 197–223.CrossRefGoogle ScholarPubMed
Cristino, F., Mathôt, S., Theeuwes, J., & Gilchrist, I. D. (2010). ScanMatch: A novel method for comparing fixation sequences. Behavior Research Methods, 42(3), 692–700.Google Scholar
Daneman, M., & Carpenter, P. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466.Google Scholar
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134.Google Scholar
de Koning, B. B., Hoogerheide, V., & Boucheix, J.-M. (2018). Developments and trends in learning with instructional video. Computers in Human Behavior, 89, 395–398.Google Scholar
de Koning, B. B., Marcus, N., Brucker, B., & Ayres, P. (2019). Does observing hand actions in animations and static graphics differentially affect learning of hand-manipulative tasks? Computers & Education, 141, 103636.CrossRefGoogle Scholar
de Koning, B. B., Rop, G., & Paas, F. (2020a). Learning from split-attention materials: Effects of teaching physical and mental learning strategies. Contemporary Educational Psychology, 61, 101873.Google Scholar
de Koning, B. B., Rop, G., & Paas, F. (2020b). Effects of spatial distance on the effectiveness of mental and physical integration strategies in learning from split-attention examples. Computers in Human Behavior, 110, 106379.Google Scholar
Demaree, D., Jarodzka, H., Brand-Gruwel, S., & Kammerer, Y. (2020). The influence of device type on querying behavior and learning outcomes in a searching as learning task with a laptop or smartphone. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR’20) (pp. 373–377). New York: Association for Computing Machinery (ACM).CrossRefGoogle Scholar
Deubel, H., & Schneider, W. X. (1996). Saccade target selection and object recognition: Evidence for a common attentional mechanism. Vision Research, 36(12), 1827–1837.CrossRefGoogle ScholarPubMed
Dewhurst, R., Foulsham, T., Jarodzka, H., Johansson, R., Holmqvist, K., & Nyström, M. (2018). How task demands influence scanpath similarity in a sequential number-search task. Vision Research, 149, 9–23.CrossRefGoogle Scholar
Duchowski, A. T. (2003). Eye Tracking Methodology: Theory and Practice. Cham: Springer.Google Scholar
Duchowski, A. T. (2018). Gaze-based interaction: A 30 year retrospective. Computers & Graphics, 73, 59–69.CrossRefGoogle Scholar
Eitel, A., Endres, T., & Renkl, A. (2020). Self-management as a bridge between cognitive load and self-regulated learning: The illustrative case of seductive details. Educational Psychology Review, 32(4), 1073–1087.Google Scholar
Eivazi, S., & Bednarik, R. (2011). Predicting problem-solving behavior and performance levels from visual attention data. In Proceedings of 2nd Workshop on Eye Gaze in Intelligent Human Machine Interaction at IUI 2011 (pp. 9–16). New York: ACM.Google Scholar
Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Manual for Kit of Factor-Referenced Cognitive Tests. Princeton, NJ: Educational Testing Service.Google Scholar
Emhardt, S., Wermeskerken, M., Scheiter, K., & Gog, T. (2020). Inferring task performance and confidence from displays of eye movements. Applied Cognitive Psychology, 34(6), 1430–1443.CrossRefGoogle Scholar
Ericsson, K. A. (2006). Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (eds.), The Cambridge Handbook of Expertise and Expert Performance (pp. 223–241). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ericsson, K. A. (2018). Capturing expert thought with protocol analysis: Concurrent verbalisations of thinking during experts’ performance on representative tasks. In Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.), Expertise and Expert Perfomance (pp. 192–212). Cambridge: Cambridge University Press.Google Scholar
Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.) (2018). Expertise and Expert Performance (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal adaption to task constraints. Annual Reviews in Psychology, 47, 273–305.Google Scholar
Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215–251.CrossRefGoogle Scholar
Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.Google Scholar
Fiedler, S., Schulte-Mecklenbeck, M., Renkewitz, F., & Orquin, J. L. (2019). Increasing reproducibility of eye-tracking studies. In Schulte-Mecklenbeck, M., Kühberger, A., & Johnson, J. G. (eds.), A Handbook of Process Tracing Methods (pp. 65–75). Abingdon: Routledge.Google Scholar
Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38(4), 281–288.Google Scholar
Fiorella, L., & Mayer, R. E. (2014). Role of expectations and explanations in learning by teaching. Contemporary Educational Psychology, 39(2), 75–85.CrossRefGoogle Scholar
Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., & Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Frontiers in Neuroscience, 8, 385.Google Scholar
Gerlic, I., & Jausovec, N. (1999). Multimedia: Differences in cognitive processes observed with EEG. Educational Technology Research and Development, 47(3), 5–14.Google Scholar
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.Google Scholar
Hansen, J. P. (1991). The use of eye mark recordings to support verbal retrospection in software testing. Acta Psychologica, 76(1), 31–49.Google Scholar
Harteis, C., Kok, E., & Jarodzka, H. (2018). New measurements of learning: Emerging chances and challenges of process measures [double Special Issue]. Frontline Learning Research, 6(2–3), 1–249.Google Scholar
Hartmann, C., Gog, T., & Rummel, N. (2020). Do examples of failure effectively prepare students for learning from subsequent instruction? Applied Cognitive Psychology, 34(4), 879–889.CrossRefGoogle Scholar
Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations – A meta-analytic review. Educational Psychology Review, 22(3), 245–269.Google Scholar
Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford: Oxford University Press.Google Scholar
Hoogerheide, V., & Roelle, J. (2020). Example-based learning: New theoretical perspectives and use-inspired advances to a contemporary instructional approach. Applied Cognitive Psychology, 34(4), 787–792.Google Scholar
Hoogerheide, V., van Wermeskerken, M., van Nassau, H., & van Gog, T. (2018). Model-observer similarity and task-appropriateness in learning from video modeling examples: Do model and student gender affect test performance, self-efficacy, and perceived competence? Computers in Human Behavior, 89, 457–464.Google Scholar
Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.CrossRefGoogle ScholarPubMed
Hummel, H. G. K., Nadolski, R. J., Eshuis, J., Slootmaker, A., & Storm, J. (2021). Serious game in introductory psychology for professional awareness: Optimal learner control and authenticity. British Journal of Educational Technology, 52(1), 125–141.Google Scholar
Jaarsma, T., Jarodzka, H., Nap, M., van Merriënboer, J. J. G., & Boshuizen, H. P. A. (2015). Expertise in clinical pathology: Combining the visual and cognitive perspective. Advances in Health Sciences Education, 20(4), 1089–1106.CrossRefGoogle ScholarPubMed
Jacob, L., Lachner, A., & Scheiter, K. (2020). Learning by explaining orally or in written form? Text complexity matters. Learning and Instruction, 68, 101344.Google Scholar
Jarodzka, H., Balslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2012). Conveying clinical reasoning based on visual observation via eye-movement modelling examples. Instructional Science, 40(5), 813–827.CrossRefGoogle Scholar
Jarodzka, H., & Boshuizen, H. P. A. (2017). Unboxing the black box of visual expertise in medicine. Frontline Learning Research, 5(3), 167–183.Google Scholar
Jarodzka, H., Holmqvist, K., & Gruber, H. (2017). Eye tracking in educational sscience: Theoretical frameworks and research agendas. Journal of Eye Movement Research, 10(1), 1–18.Google Scholar
Jarodzka, H., Holmqvist, K., & Nyström, M. (2010). A vector-based, multidimensional scanpath similarity measure. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications - ETRA’10, Austin, TX, March 2010 (pp. 211–218). https://doi.org/10.1145/1743666.1743718CrossRefGoogle Scholar
Jarodzka, H., Janssen, N., Kirschner, P. A., & Erkens, G. (2015). Avoiding split attention in computer-based testing: Is neglecting additional information facilitative?: Avoiding split attention in computer-based testing. British Journal of Educational Technology, 46(4), 803–817.CrossRefGoogle Scholar
Jarodzka, H., Scheiter, K., Gerjets, P., & van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20(2), 146–154.CrossRefGoogle Scholar
Jarodzka, H., van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2013). Learning to see: Guiding students’ attention via a Model’s eye movements fosters learning. Learning and Instruction, 25, 62–70.CrossRefGoogle Scholar
Jiang, D., Kalyuga, S., & Sweller, J. (2018). The curious case of improving foreign language listening skills by reading rather than listening: An expertise reversal effect. Educational Psychology Review, 30(3), 1139–1165.Google Scholar
Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education, 47, 100758.CrossRefGoogle Scholar
Just, M., & Carpenter, P. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8, 441–480.Google Scholar
Kalyuga, S. (2006a). Instructing and Testing Advanced Learners: A Cognitive Load Approach. New York: Nova Science Publishers.Google Scholar
Kalyuga, S. (2006b). Rapid cognitive assessment of learners’ knowledge structures. Learning and Instruction, 16(1), 1–11.Google Scholar
Kalyuga, S. (2006c). Rapid assessment of learners’ proficiency: A cognitive load approach. Educational Psychology, 26(6), 735–749.Google Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.Google Scholar
Kalyuga, S. (2008). When less is more in cognitive diagnosis: A rapid online method for diagnosing learner task-specific expertise. Journal of Educational Psychology, 100(3), 603–612.Google Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–32.Google Scholar
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.Google Scholar
Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53(3), 83–93.Google Scholar
Kant, J. M., Scheiter, K., & Oschatz, K. (2017). How to sequence video modeling examples and inquiry tasks to foster scientific reasoning. Learning and Instruction, 52, 46–58.Google Scholar
Karpf, D. A. (1973). Thinking Aloud in Human Discrimination Learning [PhD Thesis]. State University of New York.Google Scholar
Kok, E. M., & Jarodzka, H. (2017a). Before your very eyes: The value and limitations of eye tracking in medical education. Medical Education, 51(1), 114–122.Google Scholar
Kok, E. M., & Jarodzka, H. (2017b). Beyond your very eyes: Eye movements are necessary, not sufficient. Medical Education, 51(11), 1190.Google Scholar
Kostons, D., van Gog, T., & Paas, F. (2009). How do I do? Investigating effects of expertise and performance-process records on self-assessment. Applied Cognitive Psychology, 23(9), 1256–1265.Google Scholar
Kruger, J.-L., & Doherty, S. (2016). Measuring cognitive load in the presence of educational video: Towards a multimodal methodology. Australasian Journal of Educational Technology, 32(6), 19–31.Google Scholar
Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., Lee, M.-H., Chiou, G.-L., Liang, J.-C., & Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.Google Scholar
Lee, J. Y., Donkers, J., Jarodzka, H., & van Merriënboer, J. J. G. (2019). How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking. Computers in Human Behavior, 99, 268–277.CrossRefGoogle Scholar
Leijten, M., & van Waes, L. (2013). Keystroke logging in writing research: Using inputlog to analyze and visualize writing processes. Written Communication, 30(3), 358–392.Google Scholar
Leppink, J., Paas, F., van der Vleuten, C. P. M., van Gog, T., & van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.CrossRefGoogle ScholarPubMed
Lindner, M. A. (2020). Representational and decorative pictures in science and mathematics tests: Do they make a difference? Learning and Instruction, 68, 101345.Google Scholar
Litchfield, D., & Ball, L. J. (2011). Rapid communication: Using another’s gaze as an explicit aid to insight problem solving. Quarterly Journal of Experimental Psychology, 64(4), 649–656.Google Scholar
Liu, T., Lin, Y., Hsu, C., Hsu, C., & Paas, F. (2021). Learning from animations and computer simulations: Modality and reverse modality effects. British Journal of Educational Technology, 52(1), 304–317.CrossRefGoogle Scholar
Liversedge, S., Gilchrist, I., & Everling, S. (2011). The Oxford Handbook of Eye Movements. Oxford: Oxford University Press.Google Scholar
Mason, L., Pluchino, P., & Tornatora, M. C. (2015). Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning. Contemporary Educational Psychology, 41, 172–187.CrossRefGoogle Scholar
Mayer, R. E. (2005). Introduction to multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 1–16). Cambridge: Cambridge University Press.Google Scholar
Mayer, R. E. (2018). Educational psychology’s past and future contributions to the science of learning, science of instruction, and science of assessment. Journal of Educational Psychology, 110(2), 174–179.Google Scholar
McIntyre, N. A., Jarodzka, H., & Klassen, R. M. (2019). Capturing teacher priorities: Using real-world eye-tracking to investigate expert teacher priorities across two cultures. Learning and Instruction, 60, 215–224.Google Scholar
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 1–30.Google Scholar
Menendez, D., Rosengren, K. S., & Alibali, M. W. (2020). Do details bug you? Effects of perceptual richness in learning about biological change. Applied Cognitive Psychology, 34(5), 1101–1117.CrossRefGoogle Scholar
Merkt, M., Ballmann, A., Felfeli, J., & Schwan, S. (2018). Pauses in educational videos: Testing the transience explanation against the structuring explanation. Computers in Human Behavior, 89, 399–410.Google Scholar
Meyer, D. K., & Schutz, P. A. (2020). Why talk about qualitative and mixed methods in educational psychology? Introduction to special issue. Educational Psychologist, 55(4), 193–196.Google Scholar
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.Google Scholar
Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational Psychology Review, 19(3), 309–326.Google Scholar
Nelson, T. O., & Dunlosky, J. (1991). When people’s judgments of learning (JOLs) are extremely accurate at predicting subsequent recall: The “delayed-JOL effect.” Psychological Science, 2(4), 267–271.Google Scholar
Ögren, M., Nyström, M., & Jarodzka, H. (2017). There’s more to the multimedia effect than meets the eye: Is seeing pictures believing? Instructional Science, 45(2), 263–287.Google Scholar
Oliva, M., Niehorster, D. C., Jarodzka, H., & Holmqvist, K. (2017). Influence of coactors on saccadic and manual responses. I-Perception, 8(1), 1–23.CrossRefGoogle ScholarPubMed
Paas, F. (1992). Training strategies for attaining transfer of problem-solving skills in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434.Google Scholar
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.Google Scholar
Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.Google Scholar
Paivio, A. (1969). Mental imagery in associative learning and memory. Psychological Review, 76(3), 241–263.Google Scholar
Park, B., Korbach, A., & Brünken, R. (2020). Does thinking-aloud affect learning, visual information processing and cognitive load when learning with seductive details as expected from self-regulation perspective? Computers in Human Behavior, 111, 106411.Google Scholar
Peters, M., Laeng, B., Jackson, M., Zaiyouna, R., & Richardson, C. (1995). A redrawn Vandenberg and Kuse mental rotations test: Different versions and factors that affect performance. Brain and Cognition, 28, 39–58.Google Scholar
Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.Google Scholar
Rayner, K. (2009). The 35th Sir Frederick Bartlett lecture: Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62(8), 1457–1506.Google Scholar
Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12, 529–556.Google Scholar
Renkl, A., & Atkinson, R. K. (2002). Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive Learning Environments, 10(2), 105–119.Google Scholar
Rey, G. D., & Fischer, A. (2013). The expertise reversal effect concerning instructional explanations. Instructional Science, 41(2), 407–429.Google Scholar
Richter, J., & Scheiter, K. (2019). Studying the expertise reversal of the multimedia signaling effect at a process level: Evidence from eye tracking. Instructional Science, 47(6), 627–658.Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.Google Scholar
Salmerón, L., Delgado, P., & Mason, L. (2020). Using eye‐movement modelling examples to improve critical reading of multiple webpages on a conflicting topic. Journal of Computer Assisted Learning, 36(6), 1038–1051.CrossRefGoogle Scholar
Salmerón, L., Gil, L., Bråten, I., & Strømsø, H. (2010). Comprehension effects of signalling relationships between documents in search engines. Computers in Human Behavior, 26(3), 419–426.Google Scholar
Saß, S., Schütte, K., & Lindner, M. A. (2017). Test-takers’ eye movements: Effects of integration aids and types of graphical representations. Computers & Education, 109, 85–97.Google Scholar
Scarapicchia, V., Brown, C., Mayo, C., & Gawryluk, J. R. (2017). Functional magnetic resonance imaging and functional near-infrared spectroscopy: Insights from combined recording studies. Frontiers in Human Neuroscience, 11, 419.Google Scholar
Scharinger, C. (2018). Fixation-related EEG frequency band power analysis. Frontline Learning Research, 6(3), 57–71.CrossRefGoogle Scholar
Scheiter, K., Ackerman, R., & Hoogerheide, V. (2020). Looking at mental effort appraisals through a metacognitive lens: Are they biased? Educational Psychology Review, 32, 1003–1027.Google Scholar
Scheiter, K., Brucker, B., & Ainsworth, S. (2020). “Now move like that fish”: Can enactment help learners come to understand dynamic motion presented in photographs and videos? Computers & Education, 155, 103934.Google Scholar
Schmeck, A., Opfermann, M., van Gog, T., Paas, F., & Leutner, D. (2015). Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings. Instructional Science, 43(1), 93–114.Google Scholar
Schneider, S., Beege, M., Nebel, S., & Rey, G. D. (2018). A meta-analysis of how signaling affects learning with media. Educational Research Review, 23, 1–24.Google Scholar
Schneider, S., Nebel, S., Beege, M., & Rey, G. D. (2018). The autonomy-enhancing effects of choice on cognitive load, motivation and learning with digital media. Learning and Instruction, 58, 161–172.Google Scholar
Schweizer, K., & DiStefano, C. (2016). Principles and Methods of Test Construction: Standards and Recent Advances (Vol. 3). Toronto: Hogrefe.CrossRefGoogle Scholar
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.Google Scholar
Skuballa, I. T., Xu, K. M., & Jarodzka, H. (2019). The impact of co-actors on cognitive load: When the mere presence of others makes learning more difficult. Computers in Human Behavior, 101, 30–41.Google Scholar
Strijbos, J.-W., Martens, R. L., Prins, F. J., & Jochems, W. M. G. (2006). Content analysis: What are they talking about? Computers & Education, 46(1), 29–48.Google Scholar
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292.Google Scholar
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics. Applied Cognitive Psychology, 34(4), 890–905.Google Scholar
Touvinen, J. E., & Paas, F. (2004). Exploring multidimesional approaches to the efficiency of instructional conditions. Instructional Science, 32, 133–152.Google Scholar
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020). Learning analytics in European higher education – Trends and barriers. Computers & Education, 155, 103933.Google Scholar
van der Meij, H., Rensink, I., & van der Meij, J. (2018). Effects of practice with videos for software training. Computers in Human Behavior, 89, 439–445.Google Scholar
van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25(3), 785–791.Google Scholar
van Gog, T., & Paas, F. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational Psychologist, 43(1), 16–26.Google Scholar
van Gog, T., Paas, F., van Merriënboer, J. J. G., & Witte, P. (2005). Uncovering expertise-related differences in troubleshooting performance. Combining eye movement and concurrent verbal protocol data. Applied Cognitive Psychology, 19, 237–244.Google Scholar
van Laer, S., & Elen, J. (2019). The effect of cues for calibration on learners’ self-regulated learning through changes in learners’ learning behaviour and outcomes. Computers & Education, 135, 30–48.Google Scholar
van Marlen, T., van Wermeskerken, M., Jarodzka, H., & van Gog, T. (2018). Effectiveness of eye movement modeling examples in problem solving: The role of verbal ambiguity and prior knowledge. Learning and Instruction, 58, 274–283.Google Scholar
van Meeuwen, L. W., Jarodzka, H., Brand-Gruwel, S., Kirschner, P. A., de Bock, J. J. P. R., & van Merriënboer, J. J. G. (2014). Identification of effective visual problem solving strategies in a complex visual domain. Learning and Instruction, 32, 10–21.Google Scholar
van Orden, K. F., Limbert, W., Makeig, S., & Jung, T.-P. (2001). Eye activity correlates of workload during a visuospatial memory task. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(1), 111–121.Google Scholar
van Wermeskerken, M., Ravensbergen, S., & van Gog, T. (2018). Effects of instructor presence in video modeling examples on attention and learning. Computers in Human Behavior, 89, 430–438.Google Scholar
Wang, F., Zhao, T., Mayer, R. E., & Wang, Y. (2020). Guiding the learner’s cognitive processing of a narrated animation. Learning and Instruction, 69, 101357.CrossRefGoogle Scholar
Wells, A., Parong, J., & Mayer, R. E. (2020). Limits on training inhibitory control with a focused video game. Journal of Cognitive Enhancement, 5(1), 785–797.Google Scholar
Wolff, C. E., Jarodzka, H., & Boshuizen, H. P. A. (2017). See and tell: Differences between expert and novice teachers’ interpretations of problematic classroom management events. Teaching and Teacher Education, 66, 295–308.Google Scholar
Wolff, C. E., Jarodzka, H., van den Bogert, N., & Boshuizen, H. P. A. (2016). Teacher vision: Expert and novice teachers’ perception of problematic classroom management scenes. Instructional Science, 44(3), 243–265.Google Scholar
Wong, M., Castro-Alonso, J. C., Ayres, P., & Paas, F. (2018). Investigating gender and spatial measurements in instructional animation research. Computers in Human Behavior, 89, 446–456.Google Scholar
Xu, K. M., Koorn, P., de Koning, B. B., Skuballa, I. T., Lin, L., Hendrikx, M., Marsh, H. W., Sweller, J., & Paas, F. (2020). A growth mindset lowers perceived cognitive load and improves learning: Integrating motivation to cognitive load. Journal of Educational Psychology, Advance online publication. https://doi.org/10.1037/edu0000631Google Scholar

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