TY - GEN
T1 - A characterization of student's viewpoint to learning and its application to learning assistance framework
AU - Minami, Toshiro
AU - Ohura, Yoko
AU - Baba, Kensuke
N1 - Publisher Copyright:
Copyright © 2017 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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 points and bad points. These indexes show how a student's viewpoint to the class is located in comparison with other students' viewpoints.
AB - 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 points and bad points. These indexes show how a student's viewpoint to the class is located in comparison with other students' viewpoints.
KW - Educational data mining
KW - Lecture data
KW - Term-usage
KW - Text analysis
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85023754295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023754295&partnerID=8YFLogxK
U2 - 10.5220/0006389706190630
DO - 10.5220/0006389706190630
M3 - Conference contribution
AN - SCOPUS:85023754295
T3 - CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education
SP - 619
EP - 630
BT - CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education
A2 - Escudeiro, Paula
A2 - Costagliola, Gennaro
A2 - Zvacek, Susan
A2 - Uhomoibhi, James
A2 - McLaren, Bruce M.
PB - SciTePress
T2 - 9th International Conference on Computer Supported Education, CSEDU 2017
Y2 - 21 April 2017 through 23 April 2017
ER -