Optimizing learning from animation: Examining the impact of biofeedback

Learning and Instruction

Volumn 55, Issue , 2018, Pages 32-40

  Lin, Lijia ^a   Li, Ming ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Animation; Engagement; Feedback

Indexed keywords

EID: 85042622535     PISSN: 09594752     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.learninstruc.2018.02.005     Document Type: Article

References (46)

1. Abdi, H., Holm's sequential Bonferroni procedure. Salkind, N., (eds.) Encyclopedia of research design, 2010, Sage Publications, Thousand Oaks, VA, 574–578.
2. Agostinho, S., Tindall-Ford, S., Ginns, P., Howard, S.J., Leahy, W., Paas, F., Giving learning a helping hand: Finger tracing of temperature graphs on an iPad. Educational Psychology Review 27 (2015), 427–443.
3. Baker, R.S.J., Yacef, K., The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining 1:1 (2009), 3–16.
4. Berney, S., Betrancourt, M., Does animation enhance learning? A meta-analysis. Computers & Education 101 (2016), 150–167.
5. Bétrancourt, M., Tversky, B., Effect of computer animation on users’ performance: A review. Travail Humain, Le 63:4 (2000), 311–329.
6. Blumenfeld, P.C., Kempler, T.M., Krajcik, J.S., Motivation and cognitive engagement in learning environments. Sawyer, R.K., (eds.) The cambridge handbook of the learning sciences, 2004, Cambridge University Press, New York, NY, 475–488.
7. Castro-Alonso, J.C., Ayres, P., Paas, F., Learning from observing hands in static and animated versions of non-manipulative tasks. Learning and Instruction 34 (2014), 11–21.
8. Castro-Alonso, J.C., Ayres, P., Paas, F., Animations showing Lego manipulative tasks: Three potential moderators of effectiveness. Computers & Education 85 (2015), 1–13.
9. Castro-Alonso, J.C., Ayres, P., Paas, F., Comparing apples and oranges? A critical look at research on learning from statics versus animations. Computers & Education 102 (2016), 234–243.
10. Chen, C.-M., Huang, S.-H., Web-based reading annotation system with an attention-based self-regulated learning mechanism for promoting reading performance. British Journal of Educational Technology 45:5 (2014), 959–980.
11. Chi, M.T.H., Wylie, R., The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist 49:4 (2014), 219–243.
12. Chi, M.T.H., Siler, S.A., Jeong, H., Yamauchi, T., Hausmann, R.G., Learning from human tutoring. Cognit. Sci. 25:4 (2001), 471–533.
13. Cohen, V.B., A reexamination of feedback in computer-based instruction: Implications for instructional design. Educational Technology 25:1 (1985), 33–37.
14. nd ed., 1988, N.J.: L. Erlbaum Associates, Hillsdale.
15. Fiorella, L., Vogel-Walcutt, J.J., Schatz, S., Applying the modality principle to real-time feedback and the acquisition of higher-order cognitive skills. Educational Technology Research & Development 60 (2012), 223–238.
16. Fredicks, J.A., Blumenfeld, P.C., Paris, A.H., School engagement: Potential of the concept, state of the evidence. Review of Educational Research 74 (2004), 59–109.
17. Hattie, J.A., Influences on student learning (Inaugural professorial address, University of Auckland, New Zealand). 1999 Retrieved from https://www.researchgate.net/publication/237248564_Influences_on_Student_Learning [June 3, 2017].
18. Hattie, J., Timperley, H., The power of feedback. Review of Educational Research 77 (2007), 81–112.
19. Hegarty, M., Mental Animation: Inferring motion from static displays of mechanical systems. Journal of Experimental Psychology: Learning, Memory, and Cognition 18:5 (1992), 1084–1102.
20. Höffler, T.N., Leutner, D., Instructional animation versus static pictures: A meta-analysis. Learning and Instruction 17:6 (2007), 722–738.
21. Hoffman, B., Schraw, G., Conceptions of efficiency: Applications in learning and problem solving. Educational Psychologist 45:1 (2010), 1–14.
22. Holm, S., A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6:2 (1979), 65–70.
23. Kluger, A.N., DeNisi, A., Effects of feedback intervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin 119 (1996), 254–284.
24. De Koning, B.B., Tabbers, H.K., Facilitating understanding of movements in dynamic visualizations: An embodied perspective. Educational Psychology Review 23 (2011), 501–521.
25. De Koning, B.B., Tabbers, H.K., Gestures in instructional animations: A helping hand to understanding non-human movements?. Applied Cognitive Psychology 27 (2013), 683–689.
26. Lin, L., Atkinson, R.K., Christopherson, R.M., Joseph, S.S., Harrison, C.J., Animated agents and learning: does the type of verbal feedback they provide matter?. Comput. Educ. 67 (2013), 239–249.
27. Lin, L., Atkinson, R.K., Savenye, W.C., Nelson, B.C., Effects of visual cues and self-explanation prompts: empirical evidence in a multimedia environment. Interact. Learn. Environ. 24 (2016), 799–813.
28. Lin, L., Lee, C.H., Kalyuga, S., Wang, Y., Guan, S., Wu, H., The effect of learner-generated drawing and imagination in comprehending a science text. J. Exp. Educ. 85:1 (2017), 142–154.
29. Lowe, R.K., Boucheix, J.-M., A composition approach to design of educational animations. Lowe, R., Ploetzner, R., (eds.) Learning from dynamic Visualizations: Innovations in research and application, 2017, Springer, Berlin, 5–30.
30. McAuley, E., Duncan, T., Tammen, V.V., Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise & Sport 60 (1989), 48–58.
31. Moreno, R., Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional Science 32:1 (2004), 99–113.
32. Paas, F., Sweller, J., 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 (2012), 27–45.
33. Paas, F., Tuovinen, J., Tabbers, H., Van Gerven, P., Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist 38 (2003), 63–71.
34. Ryan, R.M., Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology 43 (1982), 450–461.
35. Schaeffer, L.M., Margulieux, L.M., Chen, D.-W., Catrambone, R., Feedback via educational technology. Lin, L., Atkinson, R.K., (eds.) Educational Technologies: Challenges, applications and learning outcomes, 2016, Nova Science, New York, NY, 59–72.
36. Schroth, M.L., The effects of delay of feedback on a delayed concept formation transfer task. Contemporary Educational Psychology 17 (1992), 78–82.
37. Stebner, F., Kühl, T., Hoffler, T., Wirth, J., Ayres, P., The role of process information in narrations while learning with animations and static pictures. Computers & Education 104 (2017), 34–48.
38. Sun, J.C.Y., Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data. Computers & Education 72 (2014), 80–89.
39. Sun, J.C.-Y., Yeh, K.P.C., The effects of attention monitoring with EEG biofeedback on university students’ attention and self-efficacy: The case of anti-phishing instructional materials. Computers & Education 106 (2017), 73–82.
40. Terzis, V., Moridis, C.N., Economides, A.A., The effect of emotional feedback on behavioral intention to use computer based assessment. Computers & Education 59 (2012), 710–721.
41. Turkay, S., The effects of whiteboard animations on retention and subjective experiences when learning advanced physics topics. Computers & Education 98 (2016), 102–114.
42. Van Damme, J., de Fraine, B., van Landeghem, G., Opdenakker, M.-C., Onghena, P., A new study on educational effectiveness in secondary schools in Flanders: An introduction. School Effectiveness and School Improvement 13:4 (2002), 383–397.
43. Van Gog, T., Paas, F., Marcus, N., Ayres, P., Sweller, J., The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review 21 (2009), 21–30.
44. Van Gog, T., Paas, F., van Merrienboer, J.J.G., Witte, P., Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting. Journal of Experimental Psychology: Applied 11:4 (2005), 237–244.
45. Wagner, I., Schnotz, W., Learning from static and dynamic visualizations: What kind of questions should we ask?. Lowe, R., Ploetzner, R., (eds.) Learning from dynamic Visualizations: Innovations in research and application, 2017, Springer, Berlin, 69–91.
46. Wong, A., Leahy, W., Marcus, N., Sweller, J., Cognitive load theory, the transient information effect and e-learning. Learning and Instruction 22 (2012), 449–457.