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.
|Number of pages
|CEUR Workshop Proceedings
|Published - 2016
|1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom
Duration: Apr 25 2016 → Apr 29 2016
All Science Journal Classification (ASJC) codes
- General Computer Science