Learning behavior laws may effectively predict learning performance,which is the key to developing individualized educa-tion.However,most of the research results focus on exploring students'external learning behaviors,with less analysis and mining of students'implicit behaviors.Peer assessment is an important way to represent implicit learning behaviors,so this study takes the comments and feedback generated by peer assessment as the object of analysis,adopts Quantitative Content Analysis(QCA),Lagged Sequence Analy-sis(LSA)and Social Network Analysis(SNA)to analyze the implicit behaviors of students with high-scores and low-scores from the per-spectives of emotion,cognition and metacognition,and introduces the system dynamics method to excavate the mechanism of the influence of implicit behaviors based on online peer assessment.According to the emotional,cognitive and metacognitive differences of implicit behaviors in peer assessment,we can understand the learning growth track hidden behind the data of the whole learning process,promote students'self-regulation of learning,and support teachers to conduct online academic alerts in a timely,accurate and effective manner.
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