针对"校园大数据"累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大学生异常行为预警方法(early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment,EWMAB).首先,针对学生行为画像的表征不够丰富,行为标签存在时效性、动态性等问题,建立一种基于多模态特征深度学习的跨模态学生行为画像模型;其次,针对学生异常行为预测、预警的时效性和后置性问题,在学生行为画像和学生行为分类预测基础上,提出了一种基于多模态融合的学生异常行为预警方法,通过长短期记忆神经网络(long and short term memory networks,LSTM),结合学生行为多指标数据和文本信息来解决学生异常行为预警问题;最后,本文通过应用实例验证模型以学生学习成绩异常预警为例,与其他预警算法相比,EWMAB方法可以提高预警的准确性,实现学生异常行为预警的时效性和前置性,从而使学生教育工作更具有针对性、个性化和预测性.
Early Warning of Abnormal Behavior of College Students Based on Multi-modal Fusion in Big Data Environment
In view of problems such as discreteness and sparsity in the massive data accumulated by"campus big data",how to detect potential students with abnormal behavior from the campus student groups with a large base,wide activity ranges,and strong personality has become an urgent issue to be solved in the analysis of abnormal behavior of students.This study proposes an early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment(EWMAB).First of all,in view of the insufficient representation of student behavior portraits and the timeliness and dynamics of behavior labels,a cross-modal student behavior portrait model based on multi-modal feature deep learning is established;secondly,for the timeliness and post-alarm of the prediction and early warning of abnormal behavior of students,a multi-modal fusion-based early warning method for student abnormal behaviors is proposed based on the student behavior portrait and student behavior classification prediction.Through the long and short term memory network(LSTM),combined with student behavior multi-index data and text information,the problem of early warning of students'abnormal behaviors is solved;finally,this study uses an example to verify the model and takes the early warning of abnormal academic performance of students as an example.Compared with other early warning algorithms,the EWMAB method can improve the accuracy of early warning and realize the timeliness and pre-alarm of abnormal behaviors of students so that the education of students is more targeted,personalized,and predictable.
education big datastudent's behavior portraitmulti-modal fusionabnormal behavior early warningclassification prediction
NETL
NSTL
万方数据
维普
