學術空間 / 論文 / 期刊論文
Predicting Students' Academic Procrastination in Blended Learning Course Using Homework Submission Data
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| 作 者 | Aftab Akram , Chengzhou Fu , Yuyao Li , Muhammad Yaqoob Javed , Ronghua Lin , Yuncheng Jiang , Yong Tang * |
| 期刊名稱 | IEEE Access |
| 狀 態 | 7, 2019，102487-102498 |
| 發表日期 | 2019 年 08 月 |
| 摘 要 |
Academic procrastination has been reported affecting students' performance in computersupported learning environments. Studies have shown that students who demonstrate higher procrastination tendencies achieve less than the students with lower procrastination tendencies. It is important for a teacher to be aware of the students' behaviors especially their procrastination trends. EDM techniques can be used to analyze data collected through computer-supported learning environments and to predict students' behaviors. In this paper, we present an algorithm called students' academic performance enhancement through homework late/non-submission detection (SAPE) for predicting students' academic performance. This algorithm is designed to predict students with learning dif culties through their homework submission behaviors. First, students are labeled as procrastinators or non-procrastinators using k-means clustering algorithm. Then, different classi cation methods are used to classify students using homework submission feature vectors. We use ten classi cation methods, i.e., ZeroR, OneR, ID3, J48, random forest, decision stump, JRip, PART, NBTree, and Prism. A detailed analysis is presented regarding performance of different classi cation methods for different number of classes. The analysis reveals that in general the prediction accuracy of all methods decreases with increase in the number of classes. However, different methods perform best or worst for different number of classes. |
| 關 鍵 字 | Blended learning, computer-assisted learning, educational data mining as an inquiry method, e-learning, higher education, learning management systems, online learning |
| 基金項目 | 國家自然科學基金委(U1811263)；國家自然科學基金委(61772211)； |
| 附 件 |
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