TY - GEN
T1 - Does student’s diligence to study relate to his/her academic performance?
AU - Minami, Toshiro
AU - Ohura, Yoko
AU - Baba, Kensuke
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Number JP15K00310.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - It is often pointed out that students’ academic performance becomes worse. Lack of professors’ teaching ability is often considered its major cause, and universities promote faculty development programs. According to our observation, however, the major cause is rather on student’s side, such as lack of motivation, diligence, and other attitudes toward learning. In this paper, we focus on diligence. Diligence is quite important for students to learn effectively. Among various kinds of diligence, we take two kinds of them into consideration; the length of answer text to a questionnaire, and the amount of submitted homework assignments. We investigate how these kinds of diligence of students relate each other, and how they relate to the examination score.
AB - It is often pointed out that students’ academic performance becomes worse. Lack of professors’ teaching ability is often considered its major cause, and universities promote faculty development programs. According to our observation, however, the major cause is rather on student’s side, such as lack of motivation, diligence, and other attitudes toward learning. In this paper, we focus on diligence. Diligence is quite important for students to learn effectively. Among various kinds of diligence, we take two kinds of them into consideration; the length of answer text to a questionnaire, and the amount of submitted homework assignments. We investigate how these kinds of diligence of students relate each other, and how they relate to the examination score.
KW - Attitudes to learning
KW - Educational Data Mining
KW - Lecture data analytics
KW - Retrospective evaluation
UR - http://www.scopus.com/inward/record.url?scp=85026763842&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-61845-6_5
DO - 10.1007/978-3-319-61845-6_5
M3 - Conference contribution
AN - SCOPUS:85026763842
SN - 9783319618449
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 47
BT - Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings
A2 - Takagi, Hideyuki
A2 - Shi, Yuhui
A2 - Tan, Ying
PB - Springer Verlag
T2 - 2nd International Conference on Data Mining and Big Data, DMBD 2017
Y2 - 27 July 2017 through 1 August 2017
ER -