Abstract
In this paper, we present a method of identifying learning activities that are important for students to achieve good grades. For this purpose, the data of 99 students were collected from a learning management system and an e-book system, including attendance, time on preparation and review, submission of reports, and quiz scores. We applied a support vector machine to these data to calculate a score of importance for each learning activity reflecting its contribution to the attainment of an A grade. Selecting certain important learning activities by following several evaluation measures, we verified that these learning activities played a crucial role in predicting final student achievements. One of the obtained results implies that time on preparation and review in the middle part of a course influences a student's final achievement.
| Original language | English |
|---|---|
| Pages (from-to) | 28-33 |
| Number of pages | 6 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1601 |
| Publication status | Published - Jan 1 2016 |
| Event | 1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom Duration: Apr 25 2016 → Apr 29 2016 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Science(all)
Cite this
Learning activity features of high performance students. / Okubo, Fumiya; Hirokawa, Sachio; Terai, Misato; Shimada, Atsushi; Kentaro, Kojima; Yamada, Masanori; Ogata, Hiroaki.
In: CEUR Workshop Proceedings, Vol. 1601, 01.01.2016, p. 28-33.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Learning activity features of high performance students
AU - Okubo, Fumiya
AU - Hirokawa, Sachio
AU - Terai, Misato
AU - Shimada, Atsushi
AU - Kentaro, Kojima
AU - Yamada, Masanori
AU - Ogata, Hiroaki
PY - 2016/1/1
Y1 - 2016/1/1
N2 - In this paper, we present a method of identifying learning activities that are important for students to achieve good grades. For this purpose, the data of 99 students were collected from a learning management system and an e-book system, including attendance, time on preparation and review, submission of reports, and quiz scores. We applied a support vector machine to these data to calculate a score of importance for each learning activity reflecting its contribution to the attainment of an A grade. Selecting certain important learning activities by following several evaluation measures, we verified that these learning activities played a crucial role in predicting final student achievements. One of the obtained results implies that time on preparation and review in the middle part of a course influences a student's final achievement.
AB - In this paper, we present a method of identifying learning activities that are important for students to achieve good grades. For this purpose, the data of 99 students were collected from a learning management system and an e-book system, including attendance, time on preparation and review, submission of reports, and quiz scores. We applied a support vector machine to these data to calculate a score of importance for each learning activity reflecting its contribution to the attainment of an A grade. Selecting certain important learning activities by following several evaluation measures, we verified that these learning activities played a crucial role in predicting final student achievements. One of the obtained results implies that time on preparation and review in the middle part of a course influences a student's final achievement.
UR - http://www.scopus.com/inward/record.url?scp=84977671760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977671760&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84977671760
VL - 1601
SP - 28
EP - 33
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
ER -