Investigating Video Viewing Behaviors of Students with Different Learning Approaches Using Video Analytics

Year 2018, Volume: 19 Issue: 4, 116 - 125, 01.10.2018

Gokhan Akcapınar Alper Bayazıt

https://doi.org/10.17718/tojde.471907

Cited By: 7

Abstract

The deep and surface learning approaches are closely related to the students' interaction with learning content and learning outcomes. While students with a surface approach have a tendency to acquire knowledge without questioning and to try to pass courses with minimum effort, students with a deep learning approach tend to use more skills such as problem-solving, questioning, and research. Studies show that learning approaches of students can change depending on subject, task and time. Therefore, it is important to identify students with a surface learning approach in online learning environments and to plan interventions that encourage them to use deep learning approaches. In this study, video viewing behaviors of students with deep and surface learning approaches are analyzed using video analytics. Video viewing patterns of students with different learning approaches are also compared. For this purpose, students (N=31) are asked to study a 10-minutes-long video material related to Computer Hardware course. Video interactions in this process were also recorded using video player developed by the authors. At the end of the lab session, students were asked to fill in the Learning Approach Scale by taking into account their learning approaches to the course. As a result of the study, it was observed that the students with surface approach made a statistically significant forward seek over to the students used deep learning approach while watching the video. Moreover, an investigation on the time series graphs of two groups revealed that surface learners watched the video more linearly and had fewer interactions with it. These interaction data can be modeled with machine learning techniques to predict students with surface approach and can be used to identify design problems in video materials.

Keywords

Video analytics, educational data mining, learning approach, video learning learning analytics

References

• Ak, S. (2008). Ogrenme yaklasimlarina iliskin kavramsal bir inceleme [A conceptual analysis on the approaches to learning]. Kuram ve Uygulamada Egitim Bilimleri, 8(3), 693-720. Akcapinar, G. (2015). Profiling students’ approaches to learning through moodle logs. Paper presented at the Multidisciplinary Academic Conference on Education, Teaching and Learning (MAC-ETL 2015), Prague, Czech Republic. Akcapinar, G. (2016). Predicting students' approaches to learning based on moodle logs. Paper presented at the International Conference on Education and New Learning Technologies (EDULEARN16), Barcelona, Spain. Bayazıt, A., & Akcapinar, G. (2018). Çevrimiçi Dersler için Video Analitik Aracinin Tasarlanmasi ve Gelistirilmesi [Design and Development of Video Analytics Tool for Online Cours 124 Beheshitha, S. S., Gasevic, D., & Hatala, M. (2015). A process mining approach to linking the study of aptitude and event facets of self-regulated learning. Paper presented at the Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, Poughkeepsie, New York. Biggs, J., Kember, D., & Leung, D. Y. (2001). The revised two-factor Study Process Questionnaire: R-SPQ-2F. Br J Educ Psychol, 71(1), 133-149. Biggs, J. B. (1987a). Student approaches to learning and studying. Research Monograph: ERIC. Biggs, J. B. (1987b). Study process questionnaire manual. Student Approaches to Learning and Studying: ERIC. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5), 318-331. doi:10.1504/IJTEL.2012.051815. Chen, C.-M., & Wu, C.-H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108-121. doi:http://dx.doi.org/10.1016/j.compedu.2014.08.015. Giannakos, M. N., Krogstie, J., & Aalberg, T. (2016). Video-based learning ecosystem to support active learning: application to an introductory computer science course. Smart Learning Environments, 3(1). doi:10.1186/s40561-016-0036-0. Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: an empirical study of MOOC videos. Paper presented at the Proceedings of the first ACM conference on Learning @ scale conference, Atlanta, Georgia, USA. Hamm, S. (2009). Digital audio video assessment: surface or deep learning - an investigation. RMIT University. Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks inonline lecture videos. Paper presented at the Proceedings of the first ACM conference on Learning @ scale conference, Atlanta, Georgia, USA. Kleftodimos, A., & Evangelidis, G. (2016). An interactive video-based learning environment that supports learning analytics for teaching ‘Image Editing’. Marton, F., & Saljo, R. (1976a). On qualitative difference in learning - I: Outcome and process. British Journal of Educational Psychology, 46(1), 4-11. doi:10.1111/j.2044-8279.1976.tb02980.x. Marton, F., & Saljo, R. (1976b). On qualitative differences in learning - II: Outcome as a function of the learner's conception of the task. British Journal of Educational Psychology, 46(2), 115-127. doi:10.1111/j.2044-8279.1976.tb02304.x. Ogata, H., Oi, M., Mohri, K., Okubo, F., Shimada, A., Yamada, M., Hirokawa, S. (2018). Learning Analytics for E-Book-Based Educational Big Data in Higher Education. In H. Yasuura, C.-M. Kyung, Y. Liu, & Y.-L. Lin (Eds.), Smart Sensors at the IoT Frontier (pp. 327-350). Cham: Springer International Publishing. Onder, I., & Besoluk, S. (2010). Duzenlenmis iki faktorlu calisma sureci olcegi’nin (R-SPQ2F) turkceye uyarlanmasi [Adaptation of revised two factor study process questionnaire (R-SPQ-2F) to Turkish]. Egitim ve Bilim, 35(157). Pi, Z., Hong, J., & Yang, J. (2017). Does instructor's image size in video lectures affect learning outcomes? Journal of Computer Assisted Learning, 33: 347–354. doi:10.1111/jcal.12183. Rajaratnam, N., D’cruz, S., & Chandrasekhar, M. (2013). Correlation between the learning approaches of first year medical students and their performance in multiple choice questions in physiology. NJIRM, 4(5), 43-48. 125 Schiltz, G. (2015). Video Analytics: when and how do students use tutorial videos? Paper presented at the Proc. 23rd Int. Conf. on Computers in Education ICCE. Seaton, D. T., Bergner, Y., Chuang, I., Mitros, P., & Pritchard, D. E. (2014). Who does what in a massive open online course? Commun. ACM, 57(4), 58-65. doi:10.1145/2500876. Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: towards communication and collaboration. Paper presented at the Proceedings of the 2nd International Conference on Learning Analytics and Knowledge.