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The influence of prior knowledge and viewing repertoire on learning from video

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Abstract

Video is increasingly used as an instructional tool. It is therefore becoming more important to improve learning of students from video. We investigated whether student learning effects are influenced through an instruction about other viewing behaviours, and whether these learning effects depend on their prior knowledge. In a controlled environment, 115 students watched a number of instructional videos about the technical equipment needed in a course on digital photography. Every second student was instructed about other possible viewing behaviours. A pre-post-retention test was carried out to calculate learning effects. The differences with respect to the learning effects of students who received an awareness instruction on an alternative viewing strategy were not significantly different. The differences as observed in our earlier experiment however could not be reproduced. Students with a broad viewing repertoire showed higher learning effects than students with a narrow repertoire. Furthermore, students with a strategic viewing approach also showed higher learning effects. Certain conditions have to be met: the technical and didactical quality of the video must be good, the integration in a learning task must be apparent, students must be aware of their viewing behaviour, and teachers must be aware of their students’ viewing behaviour in order to enrich the viewing repertoire of students when they have at least some basic knowledge e.g. after several lessons on the topics at hand. In future research, this study should be replicated using more complex video episodes than the instruction videos we used in our experiments that were only on the factual knowledge level of the taxonomy of Bloom. Moreover, replication of this study with a larger sample size could yield a significant improvement in learning effects. This is plausible because students need an amount of prior knowledge beyond a certain threshold value in order to be able to expand their knowledge network in their long term memory. Finally, additional media player functionality, facilitating effective student learning from video, can be described based on the results of this study.

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Notes

  1. F(3, 111) = 0.50, p = 0.68

  2. t(92.3) = 0.98, p = 0.33

  3. t(95.6) = 0.12, p = 0.91

  4. t(24) = 1.20, p = 0.09

  5. Very low: t(15.7) = 1.17, p = 0.26; Low: t(39.0) = −1.04, p = 0.30; Medium,: t(20.6) = −1.12, p = 0.28

  6. t(23.7) = −2.86, p = 0.009

References

  • Abell, M. (2006). Individualizing learning using intelligent technology and universally designed curriculum. Journal of Technology, Learning and Assessment, 5, 1–20.

    Google Scholar 

  • Atif, Y. (2011). An architectural specification for a system to adapt to learning patterns. Education and Information Technologies, 16, 259–279.

    Article  Google Scholar 

  • Ausubel, D. P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology, 51, 267–272.

    Article  Google Scholar 

  • Becta. (2005). Learning styles - an introduction to the research literature. London: Becta.

    Google Scholar 

  • Blijleven, P. (2005). Multimedia-cases: towards a bridge between theory and practice. PhD thesis University of Twente, The Netherlands.

  • Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: the classification of educational goals. New York: Longman.

    Google Scholar 

  • Clark, R., & Mayer, R. E. (2008). E-learning and the science of multimedia learning. Hoboken: Wiley.

    Google Scholar 

  • Cook, L. (1991). Learning style awareness and academic achievement among community college students. Community College Journal of Research and Practice, 15, 419–425.

    Article  Google Scholar 

  • De Boer, J. (2010). Using log files from streaming media servers for optimising the learning sequence. International Journal of Continuing Engineering Education and Life-Long Learning, 20, 40–53.

    Article  Google Scholar 

  • De Boer, J., & Tolboom, J. L. J. (2008). How to interpret viewing scenarios in log files from streaming media servers. International Journal of Continuing Engineering Education and Life-Long Learning, 18, 432–445.

    Article  Google Scholar 

  • De Boer, J., Kommers, P. A. M., & De Brock, E. O. (2011). Using learning styles and viewing styles in streaming video. Computers & Education, 56, 727–735.

    Article  Google Scholar 

  • Efklides, A. (2006). Metacognition and affect: what can metacognitive experiences tell us about the learning process? Educational Research Review, 1, 3–14.

    Article  Google Scholar 

  • Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78, 674–681.

    Google Scholar 

  • Gog, T., Kester, L., Nievelstein, F., Giesbers, B., & Paas, F. (2009). Uncovering cognitive processes: different techniques that can contribute to cognitive load research and instruction. Computers in Human Behavior, 25, 325–331.

    Article  Google Scholar 

  • Guan, Z., Lee, S., Cuddihy, E., & Ramey, J. (2006). The validity of the stimulated retrospective think-aloud method as measured by eye tracking. In Conference on Human Factors in Computing Systems (pp. 1253–1262).

  • Hattie, J.A.C. (2009). Visible learning: A synthesis of 800+ meta-analyses on achievement. Oxford, UK.

  • Kozhevnikov, M. (2007). Cognitive styles in the context of modern psychology: toward an integrated framework of cognitive style. Psychological Bulletin, 133, 464–481.

    Article  Google Scholar 

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

    Google Scholar 

  • Özpolat, E., & Akar, G. B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53, 355–367.

    Article  Google Scholar 

  • Peterson, E. R., Rayner, S. G., & Armstrong, S. J. (2009). Researching the psychology of cognitive style and learning style: is there really a future? Learning and Individual Differences, 19, 518–523.

  • Salomon, G. (1984). Television is “easy” and print is “tough”: the differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology, 76, 647–658.

    Article  Google Scholar 

  • Schiaffino, S., Garcia, P., & Amandi, A. (2008). ETeacher: providing personalized assistance to e-learning students. Computers & Education, 51, 1744–1754.

    Article  Google Scholar 

  • Tseng, J. C. R., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51, 776–786.

    Article  Google Scholar 

  • Tukey, J.W. (1977). Eploratory Data Analysis. Addison-Wesley.

  • Verhoeven, L., Schnotz, W., & Paas, F. (2009). Cognitive load in interactive knowledge construction. Learning and Instruction, 19, 369–375.

    Article  Google Scholar 

  • Vermunt, J. D. (1992). Learning styles and regulation of learning in higher education - Towards process-oriented instruction in autonomous thinking. Amsterdam/Lisse: Swets & Zeitlinger.

    Google Scholar 

  • Willingham, D.T. (2009). Why Don’t Students Like School? Wiley.

  • Zahn, C., Barquero, B., & Schwan, S. (2004). Learning with hyperlinked videos–design criteria and efficient strategies for using audiovisual hypermedia. Learning and Instruction, 14, 275–291.

    Article  Google Scholar 

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Correspondence to Jelle de Boer.

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de Boer, J., Kommers, P.A.M., de Brock, B. et al. The influence of prior knowledge and viewing repertoire on learning from video. Educ Inf Technol 21, 1135–1151 (2016). https://doi.org/10.1007/s10639-014-9372-2

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