Learning activity features of high performance students

Research output: Contribution to journalConference article

5 Citations (Scopus)

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 languageEnglish
Pages (from-to)28-33
Number of pages6
JournalCEUR Workshop Proceedings
Volume1601
Publication statusPublished - Jan 1 2016
Event1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom
Duration: Apr 25 2016Apr 29 2016

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Students
Support vector machines

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 journalConference article

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