SCHOLAT实验室Aftab博士在SCI期刊发表SCHOLAT教育大数据分析论文
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2019-08-10

      SCHOLAT团队Aftab Akram博士教育大数据分析论文“Predicting Students' Academic Procrastination in Blended Learning Course Using Homework Submission Data”在SCI 期刊IEEE Access发表,这是Aftab在博士期间发表的第4篇论文(包括两篇SCI论文,1篇CCF C类会议论文)。  


文献引用:Aftab Akram, Chengzhou Fu, Yuyao Li, Muhammad Yaqoob Javed, Ronghua Lin, Yuncheng Jiang, Yong Tang*. Predicting Students' Academic Procrastination in Blended Learning Course Using Homework Submission Data.  IEEE Access, VOLUME 7, 2019,102487-102498

ABSTRACT:Academic procrastination has been reported affecting students' performance in computer supported 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 difculties through their homework submission behaviors. First, students are labeled as procrastinators or non-procrastinators using k-means clustering algorithm. Then, different classication methods are used to classify students using homework submission feature vectors. We use ten classication 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 classication 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.


DOI: 10.1109/ACCESS.2019.2930867


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