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You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement
Journal of Computer Science and Technology ( IF 1.9 ) Pub Date : 2020-03-01 , DOI: 10.1007/s11390-020-9971-x
Xiao-Lin Li , Li Ma , Xiang-Dong He , Hui Xiong

Scholarships are a reflection of academic achievement for college students. The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester. This paper develops the Trajectory Mining on Clustering for Scholarship Assignment and Academic Warning (TMS) approach to identify the factors that affect the academic achievement of college students and to provide decision support to help low-performing students attain better performance. Specifically, we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns, learning patterns, and Internet usage patterns of students. We then apply the objective and subjective combined weighted k -means (Wosk-means) algorithm to perform clustering analysis to identify the characteristics of different student groups. Considering the difficulty in obtaining the real global positioning system (GPS) records of students, we apply manually generated spatiotemporal trajectories data to quantify the direction of trajectory deviation with the assistance of the PrefixSpan algorithm to identify low-performing students. The experimental results show that the silhouette coefficient and Calinski-Harabasz index of the Wosk-means algorithm are both approximately 1.5 times to that of the best baseline algorithm, and the sum of the squared error of the Wosk-means algorithm is only the half of the best baseline algorithm.

中文翻译:

你就是你的行为方式——大学生学业成就的时空表征学习

奖学金是大学生学业成绩的反映。传统的奖学金分配严格以期末成绩为依据,不能认可学期内成绩有上升或下降趋势的学生。本文开发了用于奖学金分配和学业警告 (TMS) 聚类的轨迹挖掘方法,以识别影响大学生学业成绩的因素,并提供决策支持以帮助表现不佳的学生获得更好的成绩。具体来说,我们首先进行特征工程以生成一组特征来表征学生的生活方式、学习模式和互联网使用模式。然后我们应用客观和主观结合的加权k-均值(Wosk-means)算法进行聚类分析,以识别不同学生群体的特征。考虑到难以获得学生真实的全球定位系统(GPS)记录,我们应用手动生成的时空轨迹数据,在 PrefixSpan 算法的帮助下量化轨迹偏差的方向,以识别表现不佳的学生。实验结果表明,Wosk-means算法的轮廓系数和Calinski-Harabasz指数均约为最佳基线算法的1.5倍,且Wosk-means算法的平方误差之和仅为其一半最好的基线算法。考虑到难以获得学生真实的全球定位系统(GPS)记录,我们应用手动生成的时空轨迹数据,在 PrefixSpan 算法的帮助下量化轨迹偏差的方向,以识别表现不佳的学生。实验结果表明,Wosk-means算法的轮廓系数和Calinski-Harabasz指数均约为最佳基线算法的1.5倍,且Wosk-means算法的平方误差之和仅为其一半最好的基线算法。考虑到难以获得学生真实的全球定位系统(GPS)记录,我们应用手动生成的时空轨迹数据,在 PrefixSpan 算法的帮助下量化轨迹偏差的方向,以识别表现不佳的学生。实验结果表明,Wosk-means算法的轮廓系数和Calinski-Harabasz指数均约为最佳基线算法的1.5倍,且Wosk-means算法的平方误差之和仅为其一半最好的基线算法。我们应用手动生成的时空轨迹数据在 PrefixSpan 算法的帮助下量化轨迹偏差的方向,以识别表现不佳的学生。实验结果表明,Wosk-means算法的轮廓系数和Calinski-Harabasz指数均约为最佳基线算法的1.5倍,且Wosk-means算法的平方误差之和仅为其一半最好的基线算法。我们应用手动生成的时空轨迹数据在 PrefixSpan 算法的帮助下量化轨迹偏差的方向,以识别表现不佳的学生。实验结果表明,Wosk-means算法的轮廓系数和Calinski-Harabasz指数均约为最佳基线算法的1.5倍,且Wosk-means算法的平方误差之和仅为其一半最好的基线算法。
更新日期:2020-03-01
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