Name
Abstract | ||
|---|---|---|
Students' behavior is important for teaching and educational research. The application of e-learning platforms provides an approach for instructors to collect students' behavior data. This paper aims to extract and analyze students' behavior under Sakai LMS (Learning Manage System). We developed an automated tool based on crawler technology to extract and preprocess learning behavior data automatically. The types of learning behavior we extracted were determined by analyzing the usage of course site in Sakai. To display students' learning behavior intuitively to instructors, we designed trace charts to visualize the data. We used the LCA (Life Cycle Assessment) methodology to analyze students' behavior in whole course duration. Machine learning model GBDT was chosen as the classifier in our model to detect well-performed students. The AUC of our evaluating results is 0.93, which proves that the students' behavior under Sakai LMS can be used to evaluate students' performance and the features we designed for evaluation are effective. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TALE.2017.8252342 | 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE) |
Keywords | DocType | ISSN |
Sakai LMS,learning behavior,performance evaluation,educational data mining | Conference | 2374-0191 |
ISBN | Citations | PageRank |
978-1-5386-0901-9 | 1 | 0.40 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Wan | 1 | 28 | 10.98 |
| Qiaoye Yu | 2 | 2 | 1.77 |
| Jun Ding | 3 | 1 | 0.40 |
| Kangxu Liu | 4 | 1 | 1.41 |