Learning Analytics for Face-to-Face Lessons

Active learning classes, which aim at increasing student participation in class, demand more management skills from the instructor than a conventional lecture class does. However, the instructor rarely recognizes how his/her lessons are different from those of others. The instructor cannot know exactly how one of his/her lessons is different from his/her previous week’s lesson. This class-to-class comparison is effective in improving classes.

We propose a method for automatically visualizing the process and content of classes. Although there are ways to visualize the contents of classes manually, these approaches involve considerable investments of time and money. Machine learning techniques can automate the visualization.


  1. Masahiro Toyoura, Mayato Sakaguchi, Xiaoyang Mao, Masanori Hanawa, Masayuki Murakami, “Visualizing the Lesson Process in Active Learning Classes,” IEEE Frontiers in Education Conference (FIE), 2016-10.
  2. Masahiro Toyoura, “Video Visualization with Computer Vision,” NII Shonan Meeting Seminar, Computer Visualization – Concepts and Challenges (046), 2014-3.
  3. Masahiro Toyoura, Satoshi Nishiguchi, Xiaoyang Mao, Masayuki Murakami, “Activity Visualization for Multiple Targets in a Video,” IEEE Pacific Visualization, PS007(Poster), 2014-3.
  4. Masahiro Toyoura, Satoshi Nishiguchi, Xiaoyang Mao, Masayuki Murakami, “ActVis: Activity Visualization in Videos ,” Cyberworlds, 2013-10.