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A Framework of Effective Science Explanation Videos Informed by Criteria for Instructional Explanations

Abstract

Science explanation videos, especially online ones, have become popular. They cover nearly all topics from school science curricula. Teachers use them in a flipped classroom. Students watch them also in their leisure time. However, there is a lack of research on guidelines that effective science explanation videos should follow. The research on instructional explanations provides essential insights into the effectiveness of science explanation videos. The goal of the present article is to develop a framework for effective science explanation videos. The development consists of two steps. First, criteria for a high explaining quality are derived from the research on effective instructional explanations. That leads to a coherent theoretical framework that requires empirical justification. The second step, therefore, is an exploration of the effects of the framework. We developed two explanation videos: one high explaining quality (HE) video following the framework and one low explaining quality (LE) video explicitly not following the framework. Both videos, however, are scientifically correct and provide the same learning opportunities. Students from a German high school assigned to two groups (n = 90; n = 86) watched, respectively, the HE and LE videos, and participated in tests for declarative and conceptual knowledge before and after watching the videos. The main result is that the HE group outperformed the LE group in the achievement of declarative knowledge (Cohen’s d = .42, p = .007). We interpret that as an argument for the validity of the framework of effective science explanation videos presented in this article.

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References

  1. Acuña, S., García-Rodicio, H., & Sánchez, E. (2011). Fostering active processing of instructional explanations of learners with high and low prior knowledge. European Journal of Psychology of Education, 26, 435–452.

  2. Altmann, A., & Nückles, M. (2017). Empirische Studie zu Qualitätsindikatoren für den diagnostischen Prozess [empirical studies on quality criteria for a diagnostic process]. In A. Südkamp & A.-K. Praetorius (Eds.), Diagnostische Kompetenz von Lehrkräften: Theoretische und methodische Weiterentwicklungen [Teachers’ diagnostic competence: theoretical and methodological developments] (pp. 134–141). Münster: Waxmann.

  3. Alty, J. L., & Coombs, M. J. (1981). Communicating with university computer users: a case study. In M. J. Coombs & J. L. Alty (Eds.), Computing skills and the user interface (pp. 7–71). London: Academic Press.

  4. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: lessons learned. The Journal of the Learning Sciences, 4, 67–207.

  5. Beheshti, M., Taspolat, A., Kaya, S. O., & Sapanca, F. H. (2018). Characteristics of instructional videos. World Journal on Educational Technology: Current Issues, 10(1), 061–069.

  6. Berland, L. K., & McNeill, K. L. (2012). For whom is argument and explanation a necessary distinction? A response to Osborne and Patterson. Science Education, 96(5), 808–813.

  7. Berland, L. K., & Reiser, B. J. (2008). Making sense of argumentation and explanation. Science Education, 93, 26–55.

  8. Brame, C. J. (2016). Effective educational videos: principles and guidelines for maximizing student learning from video content. CBE - Life Sciences Education, 15(4), 1–6.

  9. Bishop, J. L., & Verleger, M. (2013). The flipped classroom: a survey of the research, In ASEE National Conference Proceedings (Vol. 30). GA: Atlanta.

  10. Champagne, A. B., Klopfer, L. E., & Gunstone, R. F. (1982). Cognitive research and the design of science instruction. Educational Psychologist, 17, 31–53.

  11. Charalambous, C. Y., Hill, H. C., & Ball, D. L. (2011). Prospective teachers’ learning to provide instructional explanations: how does it look and what might it take? Journal of Mathematics Teacher Education, 22(1), 9–23.

  12. Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.

  13. Clark, L. A., & Watson, D. (1995). Constructing validity: basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.

  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates.

  15. Crossley, S. A., Skalicky, S., Dascalu, M., McNamara, D. S., & Kyle, K. (2017). Predicting text comprehension, processing, and familiarity in adult readers: new approaches to readability formulas. Discourse Processes: A Multidisciplinary Journal, 54, 340–359. https://doi.org/10.1080/0163853X.2017.1296264.

  16. DeJong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science: An International Journal of the Learning Sciences, 38(2), 105–134.

  17. Duffy, G., Roehler, L., Meloth, M., & Vavrus, L. (1986). Conceptualizing instructional explanation. Teaching and Teacher Education, 2, 197–214.

  18. Dutke, S., & Reimer, T. (2000). Evaluation of two types of online help for application software. Journal of Computer-Assisted Learning, 16, 307–315.

  19. Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32, 221–233.

  20. Gage, N. L. (1968). The microcriterion of effectiveness in explaining. In N. L. Gage (Ed.), Explorations of the teacher's effectiveness in explaining (pp. 1–8) Stanford Center for Research and Development in teaching.

  21. Geelan, D. (2012). Teacher explanations. In B. Fraser, K. Tobin, & C. McRobbie (Eds.), Second international handbook of science education (pp. 987–999). Dordrecht: Springer.

  22. Hattie, J. (2009). Visible learning. London: Routledge.

  23. Hempel, C., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175.

  24. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.

  25. Kamalski, J., Sanders, T., & Lentz, L. (2008). Coherence marking, prior knowledge and comprehension of informative and persuasive text: Sorting things out. Discourse Processes, 45, 323–345.

  26. Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas: (automated readability index, fog count and Flesch Reading ease formula) for navy enlisted personnel. (no. RBR–8–75). Naval Technical Training Command, Millington, TN: Research Branch.

  27. Korthagen, F. A. J., & Kessels, J. P. A. M. (1999). Linking theory and practice: changing the pedagogy of teacher education. Educational Researcher, 28(4), 4–17.

  28. Kulgemeyer, C., & Peters, C. (2016). Exploring the explaining quality of physics online explanatory videos. European Journal of Physics, 37(6), 1–14.

  29. Kulgemeyer, C., & Riese, J. (2018) From professional knowledge to professional performance: The impact of CK and PCK on teaching quality in explaining situations. Journal of Research in Science Teaching, 1–26. https://doi.org/10.1002/tea.21457.

  30. Kulgemeyer, C., & Schecker, H. (2009). Kommunikationskompetenz in der Physik: Zur Entwicklung eines domänenspezifischen Kompetenzbegriffs [Communication Competence in Physics: On the development of a domain-specific concept of competence]. Zeitschrift für Didaktik der Naturwissenschaften, 15, 131–153.

  31. Kulgemeyer, C., & Schecker, H. (2013). Students explaining science: Assessment of science communication competence. Research in Science Education, 43, 2235–2256.

  32. Kulgemeyer, C., & Tomczyszyn, E. (2015). Physik erklären – Messung der Erklärensfähigkeit angehender Physiklehrkräfte in einer simulierten Unterrichtssituation [Explaining physics: Measuring teacher trainees’ explaining skills using a simulated teaching setting]. Zeitschrift für Didaktik der Naturwissenschaften, 21(1), 111–126.

  33. Lehman, S., & Schraw, G. (2002). Effects of coherence and relevance on shallow and deep text processing. Journal of Educational Psychology, 94, 738–750.

  34. Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.

  35. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.

  36. Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638–643.

  37. Moreno, R. (2010). Cognitive load theory: more food for thought. Instructional Science: An International Journal of the Learning Sciences, 38(2), 135–141.

  38. Muller, D. (2008). Designing effective multimedia for physics education. PhD Thesis, School of Physics, University of Sydney, Australia.

  39. Nathan, M., & Petrosino, A. (2003). Expert blind spot among preservice teachers. American Educational Research Journal, 40(4), 905–928.

  40. Nielsen, W., & Hoban, G. (2015). Designing a digital teaching resource to explain phases of the moon: a case study of preservice elementary teachers making a Slowmation. Journal of Research in Science Teaching, 52(9), 1207–1233.

  41. Mayer, R. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). New York: The Cambridge University Press.

  42. Paivio, A. (1986). Mental representations: a dual coding approach, Oxford psychology series, New York: Oxford University press. Clarendon Press.

  43. Papadouris, N., Vokos, S., & Constantinou, C. (2017). The pursuit of a “better” explanation as an organizing framework for science teaching and learning. Science Education, 102, 219–237.

  44. Pekdag, B., & Le Marechal, J. F. (2010). Movies in chemistry education. Asia-Pacific Forum on Science Learning and Teaching, 11(1), 1–19.

  45. Renkl, A., Wittwer, J., Große, C., Hauser, S., Hilbert, T., Nückles, M., & Schworm, S. (2006). Instruktionale Erklärungen beim Erwerb kognitiver Fertigkeiten: sechs Thesen zu einer oft vergeblichen Bemühung [Instructional explanations and the achievement in cognitive skills: Six hypotheses on a failing attempt]. In I. Hosenfeld (Ed.): Schulische Leistung. Grundlagen, Bedingungen, Perspektiven [Achievement in schools. Models, conditions, perspectives] (pp. 205–223). Münster, Germany: Waxmann.

  46. Rescher, N. (1962). The stochastic revolution and the nature of scientific explanation. Synthese, 14, 200–215.

  47. Roelle, J., Berthold, K., & Renkl, A. (2014). Two instructional aids to optimise processing and learning from instructional explanations. Instructional Science, 42, 207–228.

  48. Sánchez, E., García Rodicio, H., & Acuña, S. R. (2009). Are instructional explanations more effective in the context of an impasse? Instructional Science, 37, 537–563.

  49. Schmidt, S. M. P., & Ralph, D. L. (2016). The flipped classroom: a twist on teaching. Contemporary Issues in Education Research, 9(1), 1–6. Retrieved from https://eric.ed.gov/?q=schmidt+ralph+flipped&id=EJ1087603. Accessed 6 Nov 2018.

  50. Schmidt-McCormack, J. A., Muniz, M. N., Keuter, E. C., Shaw, S. K., & Cole, R. S. (2017). Design and implementation of instructional videos for upper-division undergraduate laboratory courses. Chemistry Education Research and Practice, 18(4), 749–762. https://doi.org/10.1039/c7rp00078b.

  51. Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156.

  52. 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. https://doi.org/10.1007/s10956-016-9677-6.

  53. Seidel, T., Blomberg, G., & Renkl, A. (2013). Instructional strategies for using video in teacher education. Teaching and Teacher Education, 34, 56–65.

  54. Sevian, H., & Gonsalves, L. (2008). Analysing how scientists explain their research: a rubric for measuring the effectiveness of scientific explanations. International Journal of Science Education, 30(11), 1441–1467.

  55. Sorden, S. (2013). The cognitive theory of multimedia learning. In B. Irby, G. Brown, R. Lara-Alecio, & S. Jackson (Eds.), The handbook of educational theories (pp. 155–168). Charlotte: Information Age.

  56. Stockwell, B. R., Stockwell, M. S., Cennamo, M., & Jiang, E. (2015). Blended learning improves science education. Cell, 162(5), 933–936.

  57. Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12(2), 257–285.

  58. Sweller, J., & Chandler, P. (1991). Evidence for cognitive load theory. Cognition and Instruction, 8, 351–362.

  59. Talanquer, V. (2007). Explanations and teleology in chemistry education. International Journal of Science Education, 29(7), 853–870.

  60. Thomson, A., Bridgstock, R., & Willems, C. (2014). “Teachers flipping out” beyond the online lecture: maximising the educational potential of video. Journal of Learning Design, 7(3), 67–78.

  61. Treagust, D., & Harrison, A. (1999). The genesis of effective science explanations for the classroom. In J. Loughran (Ed.), Researching teaching: methodologies and practices for understanding pedagogy (pp. 28–43). Abingdon: Routledge.

  62. Webb, N. M., Ing, M., Kersting, N., & Nemer, K. M. (2006). Help seeking in cooperative learning groups. In S. A. Karabenick & R. S. Newman (Eds.), Help seeking in academic settings: goals, groups, and contexts (pp. 45–88). Mahwah, NJ: Lawrence Erlbaum Associates.

  63. Wittwer, J., & Ihme, N. (2014). Reading skill moderates the impact of semantic similarity and causal specificity on the coherence of explanations. Discourse Processes, 51, 143–166.

  64. Wittwer, J., Nückles, M., Landmann, N., & Renkl, A. (2010). Can tutors be supported in giving effective explanations? Journal of Educational Psychology, 102, 74–89.

  65. Wittwer, J., & Renkl, A. (2008). Why instructional explanations often do not work: a framework for understanding the effectiveness of instructional explanations. Educational Psychologist, 43(1), 49–64.

  66. Wolf, K., & Kratzer, V. (2015). Erklärstrukturen in selbsterstellten Erklärvideos von Kindern [Explaining structures in pupils’ self-made explanation videos.]. In K. Hugger, A. Tillmann, S. Iske, J. Fromme, P. Grell & T. Hug (Eds.), Jahrbuch Medienpädagogik 12 [Yearbook media pedagogy] (pp. 29–44) Springer.

  67. Wolf, K., & Kulgemeyer, C. (2016). Lernen mit Videos? Erklärvideos im Physikunterricht [Learning with videos? Explanation videos in physics teaching.]. Naturwissenschaften Im Unterricht Physik, 27(152), 36–41.

  68. Wolf, K. (2018). Video statt Lehrkraft? Erklärvideos als didaktisches element im Unterricht [Video replacing teachers? Explanation videos as part of instruction]. Computer + Unterricht, 109, 4-7.

  69. Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15–27.

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Correspondence to Christoph Kulgemeyer.

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Kulgemeyer, C. A Framework of Effective Science Explanation Videos Informed by Criteria for Instructional Explanations. Res Sci Educ (2018). https://doi.org/10.1007/s11165-018-9787-7

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Keywords

  • Explanation videos
  • Instructional videos
  • Instructional explanations
  • Achievement
  • Explanatory videos
  • Flipped classroom