Abstract
Pursuing online degrees and taking online courses, especially in complex subjects, can be challenging to many adult learners who have to juggle work, family responsibilities, and financial commitments. To better address the needs of these students, a series of online interactive learning modules informed by multimedia theory for teaching declarative and procedural knowledge were created and integrated in an online statistics course. Design and development was followed by evaluative efforts, which were conducted over a period of nine months with a total of 167 undergraduate students and six instructors. Students’ perceptions on the modules’ usability features (e.g., pace of audio presentations, ease of navigation, and layout) as well as on cognitive support and effectiveness of the modules to teach statistics were analyzed. Students and instructors’ reflections on their experiences with the modules were also gathered and analyzed. Both set of participants were overwhelmingly positive about their online learning and teaching experiences of statistics. Online courses and interactive multimedia firmly grounded in learning theories provide effective learning experiences and rich interaction with the course content. This is particularly important when teaching complex content as mathematics and statistics.
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Complete College America: Time is the Enemy of Graduation (2011). http://completecollege.org/resources/
National Center for Academic Transformation: How to Redesign a College Course Using NCAT’s Methodology (2014). http://www.thencat.org/Guides/AllDisciplines/ADChapterI.html
Koehler, N., Correia, A.-P., Alpay, N., LeVally, C.: Formative evaluation of a web-based multimedia intervention to support learning of statistics. In: Escudeiro, P., Costagliola, G., Zvacek, S., Uhomoibhi, J., McLaren, B.M. (eds.) Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017), vol. 1, Science and Technology Publications, Porto, pp. 92–99 (2017)
Petty, N.W.: Drill and Rote in Teaching LP and Hypothesis Testing [Web log post] (2012). https://learnandteachstatistics.wordpress.com/2012/02/
Larwin, K., Larwin, D.: A meta-analysis examining the impact of computer-assisted instruction on postsecondary statistics education: 40 years of research. J. Res. Technol. Educ. 43, 253–278 (2011)
Moore, D.S.: New pedagogy and new content: the case of statistics. Int. Stat. Rev. 65, 123–165 (1997)
Garfield, J., Chance, B., Snell, J.L.: Technology in college statistics courses. In: Holton, D., et al. (eds.) The Teaching and Learning of Mathematics at University Level. New ICMI Study Series, vol. 7. Springer, Dordrecht (2001). https://doi.org/10.1007/0-306-47231-7_32
Chance, B., Ben-Zvi, D., Garfield, J., Medina, E.: The role of technology in improving student learning of statistics. Technol. Innov. Stat. Educ. 1, 1–23 (2007)
Karpicke, J.D., Roediger, H.L.: The critical importance of retrieval for learning. Science 319, 966–968 (2008). https://doi.org/10.1126/science.1152408
Sklar, J.C., Zwick, R.: Multimedia presentations in educational measurement and statistics: design considerations and instructional approaches. J. Stat. Educ. 17 (2009)
Wender, K.F., Muehlboeck, J.S.: Animated diagrams in teaching statistics. Behav. Res. Methods Instrum. Comput. 35, 255–258 (2003)
Phye, G.D.: Academic learning and remembering. In: Phye, G.D. (ed.) Handbook of Academic Learning: The Construction of knowledge, pp. 47–64. Academic Press, San Diego (1997)
Anderson, L., Krathwohl, D.A.: Taxonomy for Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman, New York (2001)
Kirschner, P.A., Sweller, J., Clark, R.: Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching. Educ. Psychol. 41, 75–86 (2006)
Moreno, R., Mayer, R.E.: A learner-centered approach to multimedia explanations: deriving instructional design principles from cognitive theory. Interact. Multimed. Electron. J. Comput. Enhanc. Learn. 2, 12–20 (2000)
Song, S.H., Keller, J.M.: Effectiveness of motivationally adaptive computer-assisted instruction on the dynamic aspects of motivation. ETR&D 49, 5–22 (2001)
Alpay, N., Ratvatsky, P., Koehler, N., LeVally, C.: Redesigning a statistical concepts course to improve retention, satisfaction, and success rates of non-traditional undergraduate students. J. Educ. Multimed. Hypermedia 26, 5–27 (2017)
Mayer, R.E.: Multimedia Learning. Cambridge University Press, Cambridge (2009)
Stark, R., Mandl, H., Gruber, H., Renkl, A.: Conditions and effects of example elaboration. Learn. Instr. 12, 39–60 (2002)
Sweller, J.: Cognitive load during problem solving: effects on learning. Cogn. Sci. 12, 257–285 (1988)
Mayer, R.E., Moreno, R.: Nine ways to reduce cognitive load in multimedia learning. Educ. Psychol. 38, 43–52 (2003)
Mayer, R.E., Moreno, R.: Aids to computer-based multimedia learning. Learn. Instr. 12, 107–119 (2002)
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Koehler, N., Correia, AP., Alpay, N., LeVally, C. (2018). Determining the Cognitive Value of Online Interactive Multimedia in Statistics Education. In: Escudeiro, P., Costagliola, G., Zvacek, S., Uhomoibhi, J., McLaren, B. (eds) Computers Supported Education. CSEDU 2017. Communications in Computer and Information Science, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-319-94640-5_7
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DOI: https://doi.org/10.1007/978-3-319-94640-5_7
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