Uncovering student learning profiles with a video annotation tool: reflective learning with and without instructional norms
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This study explores the types of learning profiles that evolve from student use of video annotation software for reflective learning. The data traces from student use of the software were analysed across four undergraduate courses with differing instructional conditions. That is, the use of graded or non-graded self-reflective annotations. Using hierarchical cluster analysis, four profiles of students emerged: minimalists, task-oriented, disenchanted, and intensive users. Students enrolled in one of the courses where grading of the video annotation software was present, were exposed to either another graded course (annotations graded) or non-graded course (annotations not graded) in their following semester of study. Further analysis revealed that in the presence of external factors (i.e., grading), more students fell within the task-oriented and intensive clusters. However, when the external factor is removed, most students exhibited the disenchanted and minimalist learning behaviors. The findings provide insight into how students engage with the different features of a video annotation tool when there are graded or non-graded annotations and, most importantly, that having experience with one course where there are external factors influencing students’ use of the tool is not sufficient to sustain their learning behaviour in subsequent courses where the external factor is removed.
KeywordsInstructional norms Learning technology Video annotation Learning analytics Higher education
This research is in part supported by Australian Office of Learning and Teaching (Innovation and Development Grant), Canada Research Chair Program of the Government of Canada, Social Sciences and Humanities Research Council of Canada (Insight Grant), and Natural Sciences and Engineering Research Council of Canada (Discovery Grant). We also thank Thomas Dang for data extraction.
- Al-Qahtani, A. A. Y., & Higgins, S. E. (2013). Effects of traditional, blended and e-learning on students’ achievement in higher education: E-Learning, blended and traditional learning. Journal of Computer Assisted Learning, 29(3), 220–234. doi: 10.1111/j.1365-2729.2012.00490.x.CrossRefGoogle Scholar
- Aubert, O., Prié, Y., & Canellas, C. (2014). Leveraging video annotations in video-based e-learning. In 7th International Conference on Computer Supported Education, Barcelona, Spain. Retrieved from http://arxiv.org/abs/1404.4607.
- Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development, 56(1), 45–72. doi: 10.1007/s11423-007-9067-0.CrossRefGoogle Scholar
- Brooks, C., Epp, C., Logan, G., & Greer, J. (2011). The who, what, when, and why of lecture capture. In Proceedings of the First International Conference on Learning Analytics and Knowledge (pp. 86–92). Banff, Alberta, Canada: ACM. doi: 10.1145/2090116.2090128.
- Cleave, J. B., Edelson, D., & Beckwith, R. (1993). A matter of style: An analysis of student interaction with a computer-based learning environment. In American Educational Research Association (AERA) Annual Meeting, Atlanta, GA.Google Scholar
- Colasante, M., & Fenn, J. (2009). “mat”: A new media annotation tool with an interactive learning cycle for application in tertiary education. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (Vol. 2009, pp. 3546–3551). Retrieved from http://www.editlib.org/pv/31992.
- Dawson, S., Macfadyen, L., Evan, F. R., Foulsham, T., & Kingstone, A. (2012). Using technology to encourage self-directed learning: The Collaborative Lecture Annotation System (CLAS). In Ascilite conference (Vol. 2012). http://www.ascilite2012.org/images/custom/dawson,_shane_-_using_technology_to_encourage.pdf.
- Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.Google Scholar
- Garrison, D. R., & Vaughan, N. (2008). Blended learning in higher education. San Francisco: Jossey-Bass Inc., Publishers.Google Scholar
- Gašević, D., Mirriahi, N., & Dawson, S. (2014). Analytics of the effects of video use and instruction to support reflective learning. In Proceedings of the fourth international conference on learning analytics and Knowledge (pp 123–132). ACM Press. doi: 10.1145/2567574.2567590.
- Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2014). Collecting and making sense of video learning analytics. In Frontiers in Education Conference (FIE), 2014 IEEE (pp. 1–7). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7044485.
- Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. The International Review of Research in Open and Distributed Learning, 16(1). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1976.
- Gibbs, G., & Simpson, C. (2004). Conditions under which assessment supports students’ learning. Learning and Teaching in Higher Education, 1(1), 3–31.Google Scholar
- Gosper, M., Malfroy, J., & McKenzie, J. (2013). Students’ experiences and expectations of technologies: An Australian study designed to inform planning and development decisions. Australasian Journal of Educational Technology, 29(2). Retrieved from http://ascilite.org.au/ajet/submission/index.php/AJET/article/view/127.
- Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.Google Scholar
- Hulsman, R. L., Harmsen, A. B., & Fabriek, M. (2009). Reflective teaching of medical communication skills with DiViDU: Assessing the level of student reflection on recorded consultations with simulated patients. Patient Education and Counseling, 74(2), 142–149. doi: 10.1016/j.pec.2008.10.009.CrossRefGoogle Scholar
- Koestner, R., Ryan, R. M., Bernieri, F., & Holt, K. (1984). Setting limits on children’s behavior: The differential effects of controlling vs. informational styles on intrinsic motivation and creativity. Journal of Personality, 52(3), 233–248. doi: 10.1111/j.1467-6494.1984.tb00879.x.CrossRefGoogle Scholar
- Magenheim, J., Reinhardt, W., Roth, A., Moi, M., & Engbring, D. (2010). Integration of a video annotation tool into a coactive learning and working environment. In Key Competencies in the Knowledge Society (pp. 257–268). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-15378-5_25.
- Phillips, R., Maor, D., Preston, G., & Cumming-Potvin, W. (2012). Exploring learning analytics as indicators of study behaviour. In Proceedings of EdMedia: World Conference on Educational Media and Technology (pp. 2861–2867). Denver, Colorado, USA: Association for the Advancement of Computing in Education. Retrieved from http://researchrepository.murdoch.edu.au/10460/.
- Winne, P. H. (2013). Learning strategies, study skills, and self-regulated learning in postsecondary education. In M. B. Paulsen (Ed.) Higher education: Handbook of theory and research (Vol. 28, pp. 377–403). Dordrecht: Springer. Retrieved from http://link.springer.com/chapter/10.1007%2F978-94-007-5836-0_8#.
- Yin, R. K. (2009). Case study research: Design and methods (4th ed.). Thousand Oaks, CA: Sage Publications Inc.Google Scholar
- Yousef, A. M. F., Chatti, M. A., & Schroeder, U. (2014). Video-based learning: A critical analysis of the research published in 2003–2013 and future visions. In eLmL 2014, The Sixth International Conference on Mobile, Hybrid, and On-line Learning (pp. 112–119). Retrieved from http://www.thinkmind.org/index.php?view=article&articleid=elml_2014_5_30_50050.