Authors:
Toshiro Minami
1
;
Yoko Ohura
1
and
Kensuke Baba
2
Affiliations:
1
Kyushu Institute of Information Sciences, Japan
;
2
Fujitsu Laboratories, Japan
Keyword(s):
Text Mining, Text Analysis, Term-usage, Educational Data Mining, Lecture Data.
Abstract:
Due to the advancement of popularization of university education, it becomes more and more necessary for
university staff to help students by enhancing their motivations to learn in addition to training study skills.
We approach to this problem from lecture data analytics. We have been investigating students’ answer to a
term-end retrospective questionnaire, and found students’ attitude in learning and their academic performance
correlate significantly. On the basis of this finding, in this paper, we propose a framework for assisting students
to improve their learning attitude. It consists of four participants; lecturer, assisting staff including librarian,
data analysts, and learning assistance system built on top of learning management system. We discuss how the
results of our previous studies can be utilized to assist students in this framework. Further, we introduce two
indexes for measuring the weights of a student viewpoint between lecture and themselves, and between good
point
s and bad points. These indexes show how a student’s viewpoint to the class is located in comparison
with other students’ viewpoints.
(More)