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Title: Using metrics and cluster analysis for analyzing learner video viewing behaviours in educational videos
Authors: Kleftodimos, Alexandros
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Measurement
Data mining
Communications technology
Issue Date: 2014
First Page: 280
Last Page: 287
Volume Title: 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)
Abstract: On line video is a powerful tool for e-learning and this is evident from a number of reports, research papers and university initiatives, which portray that online video is becoming an important medium for delivering educational content. Therefore, research that focuses on how students view educational videos becomes of particular interest and in previous work we argued that in order to efficiently analyze learner viewing behavior we should deploy tools that log the learner activity and assist usage analysis and data mining. Working towards this direction, a framework for recording and analyzing learner behavior was presented together with findings of applying the framework into educational settings. In this paper, we continue this work by presenting a set of metrics that can be derived from the framework and be used to measure learner engagement and video popularity. These metrics in conjunction with the data mining method of clustering are then used to gain insights into learner viewing behavior.
ISBN: 978-1-4799-7100-8
Other Identifiers: 10.1109/AICCSA.2014.7073210
Appears in Collections:Department of Applied Informatics

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