Volume 29 Issue 6
Dec.  2020
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BAO Yunxia, LU Faming, WANG Yanxiao, ZENG Qingtian, LIU Cong. Student Performance Prediction Based on Behavior Process Similarity[J]. Chinese Journal of Electronics, 2020, 29(6): 1110-1118. doi: 10.1049/cje.2020.02.012
Citation: BAO Yunxia, LU Faming, WANG Yanxiao, ZENG Qingtian, LIU Cong. Student Performance Prediction Based on Behavior Process Similarity[J]. Chinese Journal of Electronics, 2020, 29(6): 1110-1118. doi: 10.1049/cje.2020.02.012

Student Performance Prediction Based on Behavior Process Similarity

doi: 10.1049/cje.2020.02.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61602279, No.61602278, No.31671588, No.61902222), the Fund of Oceanic Telemetry Engineering and Technology Research Center, State Oceanic Administration, China (No.2018002), the Shandong Provincial Postdoctoral Innovation Foundation of China (No.201603056), the Excellent Youth Innovation Team Foundation of Shandong Higher School (No.2019KJN024), and the SDUST Research Fund (No.2015TDJH102).
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  • Corresponding author: LU Faming (corresponding author) received the Ph.D. degree in computer software and theory from Shandong University of Science and Technology, Qingdao, China, in 2013. He is currently an associate professor in Shandong University of Science and Technology, Qingdao, China. His research interests are in the areas of Petri nets and process mining. (Email:fm_lu@163.com)
  • Received Date: 2019-07-15
  • Publish Date: 2020-12-25
  • Student performance prediction plays an important role in improving education quality. Noticing that students' exercise-answering processes exhibit different characteristics according to their different performance levels, this paper aims to mine the performance-related information from students' exercising logs and to explore the possibility of predicting students' performance using such process-characteristic information. A formal model of student-shared exercising processes and its discovery method from students' exercising logs are presented. Several similarity measures between students' individual exercising behavior and student-shared exercising processes are presented. A prediction method of students' performance level considering these similarity measures is explored based on classification algorithms. An experiment on real-life exercise-answering event logs shows the effectiveness of the proposed prediction method.
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