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 |
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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 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)