Self-Regulation, Knowledge, Experience: Which User Characteristics are Useful for Predicting Video Engagement?

The use of videos in education has attracted considerable research attention. However, in order to gain the most benefits, learners need to actively engage with videos. It is an important, yet challenging, task to disentangle the relation between engagement with videos and learning, and at the same time to take into account relevant individual differences in order to offer personalised support. In this paper we investigate the question: "Can user characteristics relating to self-regulation, knowledge, and experience be leveraged for predicting user engagement with videos?". Our results show that users' domain knowledge and self-regulation abilities can inform overall engagement prediction (inactive, passive and constructive learners), which makes them useful for adaptation and personalisation.

[1]  Paul R. Pintrich,et al.  Motivated Strategies for Learning Questionnaire , 2012 .

[2]  Moffat Mathews,et al.  Detecting Gaming the System in Constraint-Based Tutors , 2010, UMAP.

[3]  Antonija Mitrovic,et al.  Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching , 2017, UMAP.

[4]  Antonija Mitrovic,et al.  Reflective Experiential Learning: Using Active Video Watching for Soft Skills Training , 2016 .

[5]  D. Schacter,et al.  Enhancing attention and memory during video-recorded lectures. , 2015 .

[6]  Stephanie D. Teasley,et al.  A time series interaction analysis method for building predictive models of learners using log data , 2015, LAK.

[7]  Philip J. Guo,et al.  How video production affects student engagement: an empirical study of MOOC videos , 2014, L@S.

[8]  Aizan Yaacob,et al.  Assessing the Use of YouTube Videos and Interactive Activities as a Critical Thinking Stimulator for Tertiary Students: An Action Research. , 2014 .

[9]  Moffat Mathews,et al.  Supporting Constructive Video-Based Learning: Requirements Elicitation from Exploratory Studies , 2017, AIED.

[10]  Tobias Hecking,et al.  Role Modelling in MOOC Discussion Forums , 2017 .

[11]  Joanna Drummond,et al.  In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning , 2010, Intelligent Tutoring Systems.

[12]  Carolyn Penstein Rosé,et al.  Towards triggering higher-order thinking behaviors in MOOCs , 2016, LAK.

[13]  Kenneth R. Koedinger,et al.  Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC , 2015, L@S.

[14]  P. Pintrich.  A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). , 1991 .

[15]  Simon Buckingham Shum,et al.  Reflective writing analytics for actionable feedback , 2017, LAK.

[16]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[17]  Arthur C. Graesser,et al.  To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns , 2014, Intelligent Tutoring Systems.

[18]  Yuji Saruta,et al.  The state of , 2005 .

[19]  Ralf Klamma,et al.  Adaptive Video Techniques for Informal Learning Support in Workplace Environments , 2016, SETE@ICWL.

[20]  Christoph Lofi,et al.  Certificate Achievement Unlocked: How Does MOOC Learners' Behaviour Change? , 2017, UMAP.

[21]  P. Reimann,et al.  Process mining techniques for analysing patterns and strategies in students’ self-regulated learning , 2013, Metacognition and Learning.

[22]  Andreas Paepcke,et al.  YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips , 2015, EDM.

[23]  Ulrik Schroeder,et al.  The State of Video-Based Learning: A Review and Future Perspectives , 2014 .

[24]  Kristy Elizabeth Boyer,et al.  The Affective Impact of Tutor Questions: Predicting Frustration and Engagement , 2016, EDM.

[25]  Annie Piolat,et al.  Note Taking and Learning : A Summary of Research , 2006 .

[26]  J. Suls,et al.  Flawed Self-Assessment , 2004, Psychological science in the public interest : a journal of the American Psychological Society.

[27]  Itiel E. Dror,et al.  Optimising the use of note-taking as an external cognitive aid for increasing learning , 2009, Br. J. Educ. Technol..

[28]  Jure Leskovec,et al.  Engaging with massive online courses , 2014, WWW.

[29]  Thomas Daniel Ullmann,et al.  Reflective writing analytics: empirically determined keywords of written reflection , 2017, LAK.

[30]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[31]  Sidney K. D'Mello,et al.  Automatic gaze-based user-independent detection of mind wandering during computerized reading , 2016, User Modeling and User-Adapted Interaction.

[32]  Gila Kurtz,et al.  The Flipped-Classroom Approach: The Answer to Future Learning? , 2014 .

[33]  Tobias Hecking,et al.  Using Network-Text analysis to characterise learner engagement in active video watching , 2017 .

[34]  M. Chi,et al.  The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes , 2014 .