TY - GEN
T1 - Optimizing assignment of students to courses based on learning activity analytics
AU - Shimada, Atsushi
AU - Mouri, Kousuke
AU - Taniguchi, Yuta
AU - Ogata, Hiroaki
AU - Taniguchi, Rin Ichiro
AU - Konomi, Shin'ichi
N1 - Funding Information:
This work was supported by JST PRESTO Grant Number JPMJPR1505, JSPS KAKENHI Grand Number JP16H06304 and JP18H04125, Japan.
Funding Information:
This work was supported by JST PRESTO Grant Number JPMJPR1505, JSPS KAKENHI Grand Number JP16H0630 and JP18H04125, Japan.
Publisher Copyright:
© EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students' learning behavior patterns and teachers' approach to teaching. As a result, students may be less satisfied, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. The contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the effectiveness of assignment optimization via simulation experiments.
AB - In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students' learning behavior patterns and teachers' approach to teaching. As a result, students may be less satisfied, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. The contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the effectiveness of assignment optimization via simulation experiments.
UR - http://www.scopus.com/inward/record.url?scp=85086002542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086002542&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85086002542
T3 - EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
SP - 178
EP - 187
BT - EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
A2 - Lynch, Collin F.
A2 - Merceron, Agathe
A2 - Desmarais, Michel
A2 - Nkambou, Roger
PB - International Educational Data Mining Society
T2 - 12th International Conference on Educational Data Mining, EDM 2019
Y2 - 2 July 2019 through 5 July 2019
ER -