Title: A Study on Quantitative Representation of Student Learning Depth in Group Discussions Using a Revised Taxonomy

Year of Publication: Dec - 2020
Page Numbers: 31-37
Authors: Asako Ohno
Conference Name: The Sixth International Conference on Electronics and Software Science (ICESS2020)
- Japan

Abstract:


Today’s society is undergoing a major transformation, known as the Fourth Industrial Revolution, with various technological innovations such as AI, IoT, and Virtual Reality. In preparation for the coming Society 5.0, Japan is reforming its education system to develop human resources who can think and judge by themselves to open up new fields. One of these reforms is the promotion of active learning such as group discussions among students. However, the teachers who are responsible for these activities have been trained by the old passive education system, and it is difficult to implement them. In this paper, we report our attempt to quantitatively represent the depth of students’ learning based on Bloom’s taxonomy as an initial trial of research to estimate the depth of students’ learning based on the original content extracted from the conversations in group discussions.