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Student performance prediction plays an important role in improving education quality. Noticing that students' exercise-answering processes exhibit differ-ent 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 pro-cesses 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.