Abstract: With the rapid development of information technology and the informationization reform of education and teaching work, students’ relevant information is recorded in various forms. It is of great significance to comprehensively analyze students’ behavior and conduct performance prediction and warning in complex educational data. Aiming at the existing problems in predicting students’ grades, such as paying less attention to daily behavior data, using single data features, coarse data granularity and insufficient use of behavior data, a grade warning model based on ternary deep fusion is proposed. It starts from the perspective of group behavior and individual behavior, using the regular feature extraction module to extract regular features from group consumption behavior and activity behavior, using the activity feature extraction module to extract active features from individual consumption behavior, and using the diligence feature extraction module to extract diligent features from individual entry and exit behavior in the library. Then, the three types of features are fused to classify students and provide performance warnings. Experiments on public datasets have shown that the proposed model method has certain performance prediction and warning capabilities.

Key words: performance early warning, group behavior, feature fusion, feature selection, student behavior, education data mining

摘要： 随着信息技术的迅猛发展和教育教学工作的信息化改革，学生的相关信息以多样的形式被记录，如何在复杂的教育数据中对学生行为做出全面分析并进行成绩预测预警具有重要意义。针对现有研究在预测学生成绩时存在的对日常行为数据关注较少、对行为数据的利用不充分、使用的数据特征单一、数据粒度粗等问题，提出一种基于三元深度融合的成绩预警模型。它从群体行为和个体行为的角度出发，使用规律性特征提取模块提取群体消费行为、活动行为中的规律性特征，使用活跃性特征提取模块提取个体消费行为中的活跃性特征，使用勤奋性特征提取模块提取个体进出图书馆行为中的勤奋性特征，然后融合三类特征对学生分类进而进行成绩预警。公开数据集上的实验表明，提出的模型方法具有一定的成绩预测预警能力。

关键词: 成绩预警, 群体行为, 特征融合, 特征选择, 学生行为, 教育数据挖掘