The data in modern educational information systems are not given enough attention and are not fully utilized. Therefore, the motivations of our study are to preliminarily explore learning behavior patterns by applying process mining to educational datasets, and construct prediction models based on previous learning behavior. The data in modern educational information systems can be used by teaching managers to analyze various aspects of the educational process from different perspectives. We prepare to choose three datasets randomly which include student number, courses and grades attributes from a university's educational information systems. This paper firstly applies system clustering to give an overview of students' academic performance, and roughly determines clustering number. In order to ensure the accuracy which is relevant to the analysis of students' learning behavior patterns, a semi-biased statistic is proposed to quantitatively determine clustering number. Then, the data are clustered by fast clustering algorithm, and the clustering effect is cross-validated which is aimed at accurately analyzing the behavior patterns of student groups and using data visualization technology to visualize different student groups. Finally, the support vector machine is used to construct a classifier for predicting the learning behavior pattern, and the parameters in the support vector machine are optimized by Bayesian optimization, genetic algorithm optimization and whale optimization respectively. The research found that: 1) In the equal test of the group mean, when the significance of most courses is less than 0.05, it means that there is a significant difference among different categories. In this case, using the semi-biased statistic proposed in the paper is helpful to improve clustering effect. 2) The better the students learn, the better the clustering effect of the category which they belong to is. 3) Whale optimization algorithm works best.INDEX TERMS Educational process mining, learning behavior patterns, student profiles, support vector machines, system clustering, the semi-biased statistic.

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