Name
Abstract | ||
|---|---|---|
With the popularity of MOOCs and other online learning platforms, Educational Data Mining (EDM) has been receiving tremendous attention from researchers due to its great significance. Modeling students' effort and learning ability is a very interesting but challenging research topic. It is beneficial for student profiling, personalization recommendation, etc. Thus, numerous attempts have been devoted to this study. However, most of the existing work treat the problem in a static scenario, but they ignore the dynamic characteristics in real word applications. To address this problem, we propose a novel model to describe students' effort and learning ability (ELA) from a generative perspective. The temporal variations of both effort and learning ability of students are taken into account. To evaluate the performance of the proposed model, some extensive experiments are carried out. The experimental results have demonstrated that the proposed model outperforms other competitive methods greatly. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2937985 | IEEE ACCESS |
Keywords | DocType | Volume |
Educational data mining, effort, learning ability, computer science, information processing | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lina Gao | 1 | 1 | 0.34 |
| Zhongying Zhao | 2 | 2 | 1.06 |
| Liang Qi | 3 | 156 | 27.14 |
| Yongquan Liang | 4 | 91 | 21.60 |
| Junwei Du | 5 | 1 | 0.34 |