Learning activity features of high performance students

Fumiya Okubo, Sachio Hirokawa, Misato Oi, Atsushi Shimada, Kojima Kentaro, Masanori Yamada, Hiroaki Ogata

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)


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
Publication statusPublished - 2016
Event1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom
Duration: Apr 25 2016Apr 29 2016

All Science Journal Classification (ASJC) codes

  • General Computer Science


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