关键词: 电子签到, 属性选择, 个性特征, 成绩预测

Abstract: College course study is an important part of improving academic level. Predicting course performance of college students and warning students who have the risk of course failure have become an important topic in the study of big data in education. Electronic check-in system can collect information such as student attendance time and seat selection in classroom. This information reflects the students?? interests in studying, achievement motivation and personality characteristics, and has a strong correlation with course performance. This paper studies the prediction methods of course performance based on electronic check-in data, and builds a performance prediction model combined with the psychological test data of the students, which includes seat selection and attendance time attribute construction, prediction method design, and prediction correction modules. This paper proposes the partition method of classroom seating based on score distribution, and the correction method of partnership effect, which improve the accuracy of performance prediction. Using the real datasets, the proposed prediction model is fully verified by experiments, and the average prediction error of hundred-mark is less than 10.

Key words: electronic check-in, feature selection, personality characteristics, performance prediction