A WEB APPLICATION FOR THE ANALYSIS OF STUDENT USE OF VIDEO LEARNING RESOURCES

The importance of video as a medium for the delivery of learning resources, both in distance and mixed learning environments and in conventional classrooms (through the use of ‘flipped classrooms’, for example) is undeniable. When producing and delivering video learning resources, Mayer’s multimedia learning principles [1] are of paramount importance. In many domains Sweller’s worked-out example effect is essential, as are Chi and Renkl’s self-explanations principle (implying generation of inferences, integration of statements, and deep analysis) [2]. In order to apply Renkl’s work to online video to provide effective guidance to passive and superficial self-explainers it is necessary to acquire a better understanding of students’ video consumption patterns. The field of learning analytics provides a framework for this objective. Important work on the subject has been done by Kim et al and by Chorianopoulos [3,4]. Both have developed tools that collect that kind of information. Kim and Chorianopoulos focus on the aggregate behaviour of all students watching a video.

Our work puts the focus on individual behaviour, in order to detect patterns associated with effective learning based on self-explanations (measured in terms of increased academic performance and engagement and improvement of the learning experience). In order to do that we have developed independently a tool. We present the tool to collect individual usage pattern data and two novel visualizations for the gathered data. In order to make use with existing learning environments easier our tool works on self-hosted and Vimeo and YouTube videos embedded on a web page and uses commonly available web technologies.

While our work is still on the early steps we show as an important first result examples of collected data providing important, actionable information that expands instructors’ anticipated expectations and that we could not have obtained through strictly qualitative approaches, if only because of the inability to test all students.

We discuss on the limitations of a strictly quantitative approach based on learning analytics and suggest a mixed methodology in order to maximize our understanding of student behaviour, specially for unexpected patterns. This suggested methodology should allow us to provide effective guidance to passive and superficial self-explainers in order to improve their learning.

Finally, we explore the possibilities offered by our tool to further enhance learning from video learning resources, both for all students and for particular groups as a response to certain students’ behaviour patterns.

References:
[1] Mayer, Richard E. "Multimedia learning." Psychology of Learning and Motivation 41 (2002): 85-139.
[2] Roy, Marguerite, and Michelene TH Chi. "The self-explanation principle in multimedia learning." The Cambridge handbook of multimedia learning (2005): 271-286.
[3] Guo, Philip J., Juho Kim, and Rob Rubin. "How video production affects student engagement: An empirical study of mooc videos." Proceedings of the first ACM conference on Learning@ scale conference. ACM, 2014.
[4] Chorianopoulos, Konstantinos, Ioannis Leftheriotis, and Chryssoula Gkonela. "SocialSkip: pragmatic understanding within web video." Proceddings of the 9th international interactive conference on Interactive television. ACM, 2011.