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Multi-task MIML learning for pre-course student performance prediction

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Abstract

In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroom-teaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61701281, 61573219, and 61876098), Shandong Provincial Natural Science Foundation (ZR2016FM34 and ZR2017QF009), Shandong Science and Technology Development Plan (J18KA375), Shandong Social Science Project (18BJYJ04), and the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250100, China

    Yuling Ma, Jun Yu, Jie Guo, Gongping Yang & Yilong Yin

  2. School of Information Engineering, Shandong Yingcai College, Jinan, 250104, China

    Yuling Ma

  3. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Chaoran Cui

Authors
  1. Yuling Ma
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  2. Chaoran Cui
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  3. Jun Yu
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  4. Jie Guo
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  5. Gongping Yang
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  6. Yilong Yin
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Corresponding authors

Correspondence to Chaoran Cui or Yilong Yin.

Additional information

Yuling Ma received the Master degree in computer science and technology from Shandong University, China in 2008. She is currently pursuing her PhD degree at Shandong University, China. Her research interests are machine learning and data mining, and educational data mining, with a current specific focus on student performance prediction.

Chaoran Cui received his PhD degree in computer science from Shandong University, China in 2015. Prior to that, he received his BE degree in software engineering from Shandong University, China in 2010. During 2015–2016, he was a research fellow at Singapore Management University, Singapore. He is now a professor with School of Computer Science and Technology, Shandong University of Finance and Economics, China. His research interests include information retrieval, recommender systems, multimedia, and machine learning.

Jun Yu received the Bachelor degree in software engineering from Shandong University, China in 2017. He is currently researching machine learning and data mining in MLA laboratory as a postgraduate student at Shandong University, China. His research interests are computer vision and deep learning, with a specific focus on image analysis and understanding.

Jie Guo received the Master degree in School of Information Science and Engineering from Shandong Normal University, China in 2015. She is currently pursuing her PhD degree at Shandong University, China. Her research interests are machine learning and multimedia analysis.

Gongping Yang received his PhD degree in computer software and theory from Shandong University, China in 2007. Now he is a professor in the School of Software Engineering, Shandong University, China. His research interests are pattern recognition, image processing, biometrics, and so forth.

Yilong Yin is a professor in School of Software Engineering and the director of the MLA Lab. He received his PhD degree from Jilin University, China in 2000. From 2000 to 2002, he worked as a post-doctoral fellow in the Department of Electronic Science and Engineering, Nanjing University, China. His research interests are machine learning and data mining, computational medicine, and biometrics.

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Ma, Y., Cui, C., Yu, J. et al. Multi-task MIML learning for pre-course student performance prediction. Front. Comput. Sci. 14, 145313 (2020). https://doi.org/10.1007/s11704-019-9062-8

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