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S. Nakamura, Y. Okada

Kyushu University (JAPAN)
Recently, researches on cyber-physical systems have become more popular because electric products equip various types of sensors to gather various data through the Internet called the cyber world and by analyzing those data, it becomes possible to provide more efficient services to the physical world. As for e-learning based education environments, if we could understand students' e-learning activities by analyzing their activity data, teachers or instructors can provide more efficient instructional ways adaptably to those students. To do so, we need any tools that help us for collecting and analyzing students' e-learning activity data. We employ BookLooper[1] for collecting those data. It watches students to store their e-learning activity data such as how long and how often the students look at each page of a e-learning material like PowerPoint slides. On the other hand, one of the most effective tools for analyzing those data is the visualization tool. We employ Time-tunnel[2] as one of the visualization tools for visualizing our e-learning activity data. In this paper, we introduce BookLooper and Time-tunnel as Learning Analytics tools, explain some of their functionalities, and also explain learning analytics examples.

Learning Analytics tools, BookLooper and Time-tunnel:
Our university employs an e-books viewer called BookLooper produced by KYOCERA Communication Systems Co., Ltd, one of the commercial cloud services in which users can read electronic textbooks registered into the service through the Internet. Users can access this service on any platform, i.e., any device and any OS. This service gathers users' activity data of reading electronic textbooks, e.g., how long each user reads an electronic textbook, from and to which pages of the textbook the user traverse. The attributes of such data are date, time, user name, material name that the user access, activity which is a sequence of reading page indices.
Time-tunnel is a visualization tool for time-series numerical data proposed by Okada et al. Our e-learning activity data is also time-series numerical data so that Time-tunnel is suitable for that. Time-tunnel is mainly composed from three components, those are called Data-wing, Time-plane and Time-bar respectively. One time-series numerical data is displayed as one chart on each Data-wing. By rotating several Data-wings and putting them together, we can compare the multiple time-series numerical data displayed on them.

Learning Analytics Examples:
By investigating relationships between learning patterns of students and their obtained grades, it becomes possible to find out the learning patterns of higher achievable students, what pages of slides they spend much time for, and the learning patterns of lower achievable students, how different from the higher achievable students' patterns. We will show these learning analytics examples using BookLooper and Time-tunnel. These learning analytics can make teachers or instructors provide more efficient instructional ways adaptably to the target students.

[1] BookLooper, (on 15th, Dec., 2016)
[2] Akaishi, M. and Okada, Y., Time-tunnel: visual analysis tool for time-series numerical data and its aspects as multimedia presentation tool, Proceedings of Eighth International Conference on Information Visualisation (IV 2004), pp. 456-461, 2004.