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A Clickstream Analytical Tool for Video Lectures

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The paper presents an ongoing development of an analytical tool for the clickstreams of video lectures. This work was motivated by the increasing number of video tutorials in graphical software applications. Although the clickstreams have been used to examine students’ detailed behaviors, few analytical tools have been designed from the instructors’ viewpoint. The tool we have developed includes four major interfaces: (1) accumulated behavior, (2) individual time history, (3) video control, and (4) a note-taking area. We also designed a global time controller, which users may move in all of the interfaces except the note-taking area, thus synchronizing the time stamps among the other interfaces. An actual CAD video lecture clickstream of 53 students and 103,679 clicks was used to validate the tool. We found that the analytical tool can help identify students' behaviors.

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A Clickstream Analytical Tool for Video Lectures

  1. 1. A Clickstream Analytical Tool for Video Lectures Yao-Yu Yang, Chia-Hsin Liu, Yi-Hsuan Lin, Shih-Chung Kang Computer-Aided Engineering Div. of Civil Engineering Dept., National Taiwan University Yao-Yu (Ben) Yang, Ph.D student, NTU 16th October, 2016 web: yyben.tw
  2. 2. Students' Watching Patterns are Important for instructors. More than 60% MOOC instructors thought students' watching patterns would help them: (1)find struggling parts, most engaging content, and trouble students (2)improve topic presentations (3)understand some assignment issues 2 Stephens-Martinez, K., et al. (2014). Monitoring moocs: which information sources do instructors value? Proceedings of the first ACM conference on Learning@ scale conference, ACM.
  3. 3. Students' Watching Patterns are Important for instructors. More than 60% MOOC instructors thought students' watching patterns would help them: (1)find struggling parts, most engaging content, and trouble students (2)improve topic presentations (3)understand some assignment issues 3 However, the instructors need analytical tools to identify students’ learning process from the activity log.
  4. 4. Research goal To identify students' watching patterns through the log of individual time history. 4
  5. 5. • Seeking actions indicate that the students were definitely watching at the video. We focused on seek log 5 Seeking action
  6. 6. 6 We used the seek log which comes from one of video lectures of Engineering Graphics. Engineering Graphics Course - lecture topic: Dimension Style In the video, 53 students applied seeking actions.
  7. 7. 7 The seek log is hard to read. We applied 2 visual approaches to plot the seek log in order to obtain the individual time history. The seek log of the video
  8. 8. Visual approach I Backward seeking Time axis Forward seeking 8 AB < A B< A: Control Start Point B: Control End Point
  9. 9. Visual approach I Backward seeking Time axis Forward seeking 9 AB A B A: Control Start Point B: Control End Point
  10. 10. Visual approach I 10 Act.3: Forward seeking. Act.4: Backward seeking. Act.1:Continuously seeking forward. Act.2: Jumped to the beginning An example of individual time history
  11. 11. Visual approach I 11 Animation makes the individual time history clear. The animation of the individual time history for student ID 51.
  12. 12. Visual approach II 12 Total number of views Total number of skips The accumulated behavior chart shows the total number of views and skips for every second of the video.
  13. 13. 13 Individual time history Accumulated behavior chart Video content Note-taking area The clickstream analytical tool
  14. 14. Type I:Overview first Type II:Smoothly watch Type III: Skip and never watch a segment Type IV: Concentrate on a segment 14 Four watching patterns recognized
  15. 15. What is the video content for those extreme values on the accumulated behaviour chart? 15 Accumulated behavior chart Video content
  16. 16. What is the video content for those extreme values on the accumulated behaviour chart? 16 Accumulated behavior chart Video content
  17. 17. 17 The lowest part was describing the topic of the video lecture at the beginning The peak was talking about spacing of baselines in CAD, which was a detailed operation. What is the video content for those extreme values on the accumulated behaviour chart?
  18. 18. What is the video content for those extreme values on the accumulated behaviour chart? 18 Accumulated behavior chart Video content
  19. 19. 19 The highest was informing the numbers of a parameter setting which had already shown on the video. The lowest was introducing annotation lines in detail. What is the video content for those extreme values on the accumulated behaviour chart?
  20. 20. 20 •The two visual approaches we developed help instructors understand students’ learning process. •We summarized video watching patterns into four types, and found out the popular and boring part of the video. Conclusion
  21. 21. 21 •CAD instructors may evaluate their teaching strategy and the effectiveness of the video lectures. •We plan to analyze all the video lectures of the Engineering Graphics course for a bigger picture. Implication and future work
  22. 22. Thank you DEMO: yyben.tw/projects/FinalProject/index.html yyben@caece.net

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