access icon free Student Performance Prediction Based on Behavior Process Similarity

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.

Inspec keywords: computer aided instruction; data mining; educational courses

Other keywords: performance-related information; similarity measures; student-shared; performance levels; process-characteristic information; prediction method; student performance prediction; real-life exercise-answering event logs; behavior process similarity

Subjects: Data handling techniques; Knowledge engineering techniques; Computer-aided instruction

http://iet.metastore.ingenta.com/content/journals/10.1049/cje.2020.02.012
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