ABSTRACT
Understanding why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.
- Alan Baddeley. 2003. Working memory: looking back and looking forward. Nature reviews neuroscience 4, 10 (2003), 829--839.Google Scholar
- Rebekah Benjamin. 2012. Reconstructing Readability: Recent Developments and Recommendations in the Analysis of Text Difficulty. Educational Psychology Review 24, 1 (2012), 63--88.Issue 1.Google ScholarCross Ref
- Derek O Bruff, Douglas H Fisher, Kathryn E McEwen, and Blaine E Smith. 2013. Wrapping a MOOC: Student perceptions of an experiment in blended learning. MERLOT Journal of Online Learning and Teaching 9, 2 (2013), 187--199.Google Scholar
- Marc Brysbaert, Denis Drieghe, and Françoise Vitu. 2005. Word skipping: Implications for theories of eye movement control in reading. Oxford University Press, Chapter 6, 1--29.Google Scholar
- Jeanne S. Chall and Edgar Dale. 1995. T1 - Readability Revisited: The New Dale-Chall Readability Formula. PB - Brookline Books, Cambridge, Mass.Google Scholar
- Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, and David Sontag. 2011. Personalizing web search results by reading level. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM'11), Bettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, and Ian Ruthven (Eds.). ACM, New York, NY, USA, 403--412. Google ScholarDigital Library
- Edward Fry. 2002. Readability versus leveling. Reading Teacher 56, 3 (2002), 286.Google Scholar
- E. Gibson. 2000. The dependency locality theory: A distance-based theory of linguistic complexity. In Image, language, brain: Papers from the first mind articulation project symposium. 95--126.Google Scholar
- Paul Ginns. 2006. Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction 16, 6 (2006), 511--525.Google ScholarCross Ref
- Philip J. Guo, Juho Kim, and Rob Rubin. 2014. How Video Production Affects Student Engagement: An Empirical Study of MOOC Videos. In Proceedings of the First ACM Conference on Learning @ Scale Conference (L@S'14). ACM, New York, NY, USA, 41--50. Google ScholarDigital Library
- Glyn Humphreys, Lindsay Evett, and David Taylor. 1982. Automatic phonological priming in visual word recognition. Memory & Cognition 10 (1982), 576--590.Issue 6.Google ScholarCross Ref
- Ross Ihaka and Robert Gentleman. 1996. R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics 5, 3 (1996), 299--314.Google ScholarCross Ref
- Albrecht Inhoff and Keith Rayner. 1986. Parafoveal word processing during eye fixations in reading: Effects of word frequency. Attention, Perception, & Psychophysics 40, 6 (1986), 431--439.Issue 6.Google ScholarCross Ref
- T. Florian Jaeger and Harry Tily. 2011. On language utility: processing complexity and communicative efficiency. Wiley Interdisciplinary Reviews: Cognitive Science 2, 3 (2011), 323--335.Google ScholarCross Ref
- M.A. Just and P.A. Carpenter. 1980. A theory of reading: From eye fixations to comprehension. Psychological Review 87 (1980), 329--354.Google ScholarCross Ref
- Judy Kay, Peter Reimann, Elliot Diebold, and Bob Kummerfeld. 2013. MOOCs: So Many Learners, So Much Potential ... IEEE Intelligent Systems 28, 3 (2013), 70--77. Google ScholarDigital Library
- Hanan Khalil and Martin Ebner. 2014. MOOCs Completion Rates and Possible Methods to Improve Retention - A Literature Review. In Proceedings of EdMedia: World Conference on Educational Media and Technology 2014, Jarmo Viteli and Marianna Leikomaa (Eds.). Association for the Advancement of Computing in Education (AACE), Tampere, Finland, 1305--1313.Google Scholar
- Juho Kim, Philip J. Guo, Daniel T. Seaton, Piotr Mitros, Krzysztof Z. Gajos, and Robert C. Miller. 2014. Understanding In-video Dropouts and Interaction Peaks Inonline Lecture Videos. In Proceedings of the First ACM Conference on Learning @ Scale Conference (L@S'14). ACM, New York, NY, USA, 31--40. Google ScholarDigital Library
- René F Kizilcec, Kathryn Papadopoulos, and Lalida Sritanyaratana. 2014. Showing face in video instruction: effects on information retention, visual attention, and affect. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2095--2102. Google ScholarDigital Library
- Vitomir Kovanović, Dragan Gavsević, Shane Dawson, Srećko Joksimović, Ryan S. Baker, and Marek Hatala. 2015. Penetrating the Black Box of Time-on-task Estimation. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (LAK'15). ACM, New York, NY, USA, 184--193. Google ScholarDigital Library
- Kerry Ledoux, C. Christine Camblin, Tamara Y. Swaab, and Peter C. Gordon. 2006. Reading Words in Discourse: The Modulation of Lexical Priming Effects by Message-Level Context. Behavioral and Cognitive Neuroscience Reviews 5, 3 (2006), 107--127.Google ScholarCross Ref
- Richard L. Lewis, Shravan Vasishth, and Julie A. Van Dyke. 2006. Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences 10, 10 (2006), 447 -- 454.Google ScholarCross Ref
- Nan Li, Łukasz Kidzinski, Patrick Jermann, and Pierre Dillenbourg. 2015. MOOC Video Interaction Patterns: What Do They Tell Us? In Design for Teaching and Learning in a Networked World, Gránne Conole, Tomavz Klobuvcar, Christoph Rensing, Johannes Konert, and Élise Lavoué (Eds.). Lecture Notes in Computer Science, Vol. 9307. Springer International Publishing, 197--210.Google Scholar
- Bertha A. Lively and Sidney L. Pressey. 1923.showarticletitleA Method for Measuring the "Vocabulary Burden" of Textbooks. Educational Administration and Supervision 9, 7 (1923), 389--398.Google Scholar
- Anoush Margaryan, Manuela Bianco, and Allison Littlejohn. 2015. Instructional quality of Massive Open Online Courses (MOOCs). Computers & Education 80 (2015), 77 -- 83.Google ScholarDigital Library
- Richard E Mayer and Roxana Moreno. 2003. Nine ways to reduce cognitive load in multimedia learning. Educational psychologist 38, 1 (2003), 43--52.Google Scholar
- Roxana Moreno and Richard E Mayer. 1999. Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of educational psychology 91, 2 (1999), 358.Google ScholarCross Ref
- Justin Reich. 2015. Rebooting MOOC Research. Science 347, 6217 (2015), 34--35.Google Scholar
- Erik D. Reichle, Alexander Pollatsek, Donald L. Fisher, and Keith Rayner. 1998. Toward a model of eye movement control in reading. Psychological Review 105, 1 (1998), 125--157.Google ScholarCross Ref
- Claude E. Shannon. 1948. A Mathematical Theory of Communication. Bell System Technical Journal 27, 7, 10 (Jul, Oct 1948), 379--423, 625--656.Google ScholarCross Ref
- Larry L Shirey and Ralph E Reynolds. 1988. Effect of interest on attention and learning. Journal of Educational Psychology 80, 2 (1988), 159.Google ScholarCross Ref
- Tanmay Sinha, Patrick Jermann, Nan Li, and Pierre Dillenbourg. 2014. Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions. In Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses. Doha, Qatar.Google ScholarCross Ref
- Frans Van der Sluis, Egon L. Van den Broek, Richard J. Glassey, Elisabeth M. A. G. van Dijk, and Franciska M. G. de Jong. 2014. When Complexity becomes Interesting. Journal of the American Society for Information Science and Technology 65, 7 (2014), 1478--1500.Google Scholar
- Tim Vor der Brück, Sven Hartrumpf, and Hermann Helbig. 2008. A Readability Checker with Supervised Learning Using Deep Indicators. Informatica 32, 4 (2008), 429--435.Google Scholar
- Torsten Zesch, Christof Müller, and Iryna Gurevych. 2008. Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary. In Proceedings of the 6th International Conference on Language Resources and Evaluation. Marrakech, Morocco.Google Scholar
Index Terms
- Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time
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