Impact of Lecturer’s Discourse for Students’ Video Engagement: Video Learning Analytics Case Study of MOOCs

Main Article Content

Thushari Atapattu
Katrina Falkner

Abstract

Lecture videos are amongst the most widely used instructional methods in present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, students’ engagement behaviour with videos directly impacts the students’ success or failure and accordingly, in-video dropouts positively correlate with dropout from MOOCs. The primary focus of previous video learning analytics studies is on analysing video engagement behaviour using explicit factors (i.e. views or annotations). Limited research studies focus on implicit video learning analytics (e.g. pause, seek, content type) and their impact on students’ success, with existing studies addressing video interactions and their relationship with visual transitions. We aim to explore the association between video interactions and non-visual (i.e. verbal or discourse) transition. This research focuses on text (e.g. cohesion and syntactic complexity) and spoken (e.g. speaking rate) discourse features of lecture videos. We conduct a fine-grained analysis of 3.4 million video interactions of two AdelaideX MOOCs – Programming (Code101x) and Cyber, Surveillance and Security (Cyber101x). According to our results, some discourse features (e.g. lexical diversity and causal connectives) demonstrate statistically significant correlation with video interactions. We present insights for educational video design implications based on discourse processing theories.

Article Details

How to Cite
Atapattu, T., & Falkner, K. (2018). Impact of Lecturer’s Discourse for Students’ Video Engagement: Video Learning Analytics Case Study of MOOCs. Journal of Learning Analytics, 5(3), 182–197. https://doi.org/10.18608/jla.2018.53.12
Section
Research Papers

References

Atapattu, T., K. Falkner and H. Tarmazdi (2016). Topic-wise classification of MOOC discussions: A visual analytics approach. Proceedings of the 9th International conference on Educational Data Mining, Raleigh, NC, USA.

Baayen, R. H., R. Piepenbrock and L. Gulikers (1995). The CELEX lexical database. Linguistic Data Consortium. Philadelphia, PA.

Breslow, L. B., D. E. Pritchard, J. DeBoer, G. S. Stump, A. D. Ho and D. T. Seaton (2013). "Studying learning in the worldwide classroom: Research into edX's first MOOC." Research & Practice in Assessment 8: 13-25.

Cain, K. and H. M. Nash (2011). "The influence of connectives on young readers' processing and comprehension of text." Journal of Educational Psychology 103 (2): 429 - 441.

Colasante, M. (2011). "Using video annotation to reflect on and evaluate physical education pre-service teaching practice " Australasian Journal of Educational Technology 27(1): 66-88.

de Barba, P. G., G. E. Kennedy and M. D. Ainley (2016). "The role of students' motivation and participation in predicting performance in a MOOC." Journal of Computer Assisted Learning 32(3): 218-231.

Diwanji, P., B. P. Simon, M. Märki, S. Korkut and R. Dornberger (2014). Success factors of online learning videos. 2014 International Conference on Interactive Mobile Communication Technologies and Learning

Graesser, A. C., Z. Cai, M. Louwerse and F. Daniel (2006). "Question Understanding Aid (QUAID): A web facility that helps survey methodologists improve the comprehensibility of questions." Public Opinion Quarterly 70: 3–22.

Graesser, A. C., D. S. McNamara and J. M. Kulikowich (2011). "Coh-Metrix: Providing Multilevel Analyses of Text Characteristics " Educational Researcher 40(5 ): 223-234

Grigoras, R., V. Charvillat and M. Douze (2002). Optimizing hypervideo navigation using a Markov decision process approach. Proceedings of the tenth ACM international conference on Multimedia. Juan-les-Pins, France, ACM: 39-48.

Guo, P. J., J. Kim and R. Rubin (2014). How video production affects student engagement: an empirical study of MOOC videos. Proceedings of the first ACM conference on Learning @ scale conference. Atlanta, Georgia, USA, ACM: 41-50.

Halawa, S., D. Greene and J. Mitchell (2014). "Dropout prediction in MOOCs using learner activity features." eLearning papers.

Hsin, W. and J. Cigas (2013). "Short videos improve student learning in online education." Journal of Computing Sciences in Colleges 28(5): 253-259.

Hua, K. (2015). "Education as entertainment: YouTube sensations teaching the future.", 2016, from http://www.forbes.com/sites/karenhua/2015/06/23/education-as-entertainment-youtube-sensations-teaching-the-future/#57bd75574ca1.

Jurafsky, D. and J. Martin (2008). Speech and language processing. Englewood, NJ, Prentice Hall.

Kim, J., P. J. Guo, D. T. Seaton, P. Mitros, K. Z. Gajos and R. C. Miller (2014). Understanding in-video dropouts and interaction peaks in online lecture videos. Proceedings of the first ACM conference on Learning at scale conference. Atlanta, Georgia, USA, ACM: 31-40.

Kim, J., S. Li, C. J. Cai, K. Z. Gajos and R. C. Miller (2014). Leveraging video interaction data and content analysis to improve video learning. CHI 2014 Learning Innovation at Scale workshop. Toronto, Canada.

Kintsch, W., E. Kozminsky, W. J. Streby, G. McKoon and J. M. Keenan (1975). "Comprehension and recall of text as a function of content variables." Journal of Verbal Learning and Verbal Behaviour 14: 196-214.

Kizilcec, R. F., C. Piech and E. Schneider (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. Proceedings of the Third International Conference on Learning Analytics and Knowledge. Leuven, Belgium, ACM: 170-179.

Klare, G. R. (1974–1975 ). "Assessing readability." Reading Research Quarterly 10: 62-102.

Landauer, T., D. S. McNamara, S. Dennis and W. Kintsch (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ, Erlbaum.

Li, N., L. Kidzinski, P. Jermann and P. Dillenbourg (2015). MOOC Video Interaction Patterns: What Do They Tell Us? Proceedings of the 10th European Conference on Technology Enhanced Learning Toledo, Spain.

Louwerse, M. M. (2001). "An analytic and cognitive parameterization of coherence relations." Cognitive Linguistics 12: 291-315.

Mayer, R. E. (2009). Multimedia learning. Cambridge, UK, Cambridge University Press.

McCarthy, P. M. and S. Jarvis (2010). "MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment." Behavior Research Methods 42(2): 381-392.

McNamara, D. S., A. C. Graesser, P. M. McCarthy and Z. Cai (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge, M.A, Cambridge University Press.
McNamara, D. S., M. M. Louwerse, P. M. McCarthy and A. C. Graesser (2010). "Coh-Metrix: Capturing Linguistic Features of Cohesion." Discourse Processes 47(4): 292-330.

Millis, K. K., J. M. Golding and G. Barker (1995). "Causal connectives increase inference generation." Discourse Processes 20(1): 29-49.

Mirriahi, N. and L. Vigentini (2017). Analytics of Learner Video Use. Handbook of Learning Analytics. C. Lang, G. Siemens, A. Wise and D. Gašević.

Mu, X. (2010). "Towards effective video annotation: An approach to automatically link notes with video content." Comput. Educ. 55(4): 1752-1763.

Pennebaker, J. W., R. J. Booth and M. E. Francis (2007) "LIWC2007: Linguistic inquiry and word count."

Risko, E. F., T. Foulsham, S. Dawson and A. Kingstone (2013). "The Collaborative Lecture Annotation System (CLAS): A New TOOL for Distributed Learning." IEEE Transactions on Learning Technologies 6(1): 4-13.

Rosé, C. P. and O. Ferschke (2016). "Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses." International Journal of Artificial Intelligence in Education 26(2): 660-678.

Sinha, T., P. Jermann, N. Li and P. Dillenbourg (2014). Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions. Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses. Doha, Qatar.

Templin, M. (1957). Certain language skills in children: Their development and interrelationships. Minneapolis, MN, The University of Minnesota Press.

Turney, P. D., Y. Neuman, D. Assaf and Y. Cohen (2011). Literal and metaphorical sense identification through concrete and abstract context. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, United Kingdom, Association for Computational Linguistics: 680-690.

Wang, Y. and R. Baker (2015). "Content or platform: Why do students complete MOOCs?" MERLOT Journal of Online Learning and Teaching 11(1).

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