PKULAE: A Learning Attitude Evaluation Method Based on Learning Behavior
Learning attitude is an important factor related to students’ academic achievement. The existing E-learning systems has paid little attention to the students’ learning attitude, which lacks the ability of supervision for students’ learning behavior. This paper designs a learning attitude evaluation method PKULAE based on 17 kinds of learning behaviors. This method analyses the students’ learning behavior from both global and local aspects. It judges the student’s learning attitude from global and local aspects by regression analysis and GBDT regression tree constructed by the students’ behavior characteristic matrix. Finally, it determines the students’ learning attitude in a certain period by voting. The paper classifies learning attitudes into positive attitudes and negative attitudes. We did experiments based on the online behavior data of 125 students in Peking University. The result shows that PKULAE method has been significantly improved compared with GBDT and RF. When the recall and the TNR are no less than GBDT and RF, the accuracy is 0.778, which is at least 0.07 higher than GBDT and RF.
KeywordsLearning attitude Learning behavior analysis Regression analysis Software engineering education GBDT regression tree
This paper was supported by National Key Research and Development Program of China (Grant No. 2017YFB1402400), Ministry of Education “Tiancheng Huizhi” Innovation Promotes Education Fund (Grant No. 2018B01004), National Natural Science Foundation of China (Grant No. 61402020), and CERNET Innovation Project (Grant No.NGII20170501).
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