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<p>Educational Process Mining aims at supporting educational processes by leveraging historical data of students’ behaviors. In this work, we show how to leverage behaviors characterizing students’ first year of university to predict whether they will graduate on time. We transform the sequence of exams a student has taken into an Instance Graph modeling both sequential and concurrent behaviors. We then compare two prediction approaches, i.e., i) providing the graphs as input to a graph neural network, and ii) extracting relevant subprocesses used as features for traditional machine learning approaches. The results show that graph neural network performs best, achieving a 93.56% accuracy against the 82.43% achieved by machine learning approaches using subprocesses. Nevertheless, we advocate that these subprocesses have value both to provide descriptive insights on students’ early careers and to explain the results of the graph neural network.</p>

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Predicting students’ academic performance based on early career behaviors: a process-aware approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月10日

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  • Claudia Diamantini,
  • Laura Genga,
  • Alessandro Mele,
  • Alex Mircoli,
  • Domenico Potena

摘要

Educational Process Mining aims at supporting educational processes by leveraging historical data of students’ behaviors. In this work, we show how to leverage behaviors characterizing students’ first year of university to predict whether they will graduate on time. We transform the sequence of exams a student has taken into an Instance Graph modeling both sequential and concurrent behaviors. We then compare two prediction approaches, i.e., i) providing the graphs as input to a graph neural network, and ii) extracting relevant subprocesses used as features for traditional machine learning approaches. The results show that graph neural network performs best, achieving a 93.56% accuracy against the 82.43% achieved by machine learning approaches using subprocesses. Nevertheless, we advocate that these subprocesses have value both to provide descriptive insights on students’ early careers and to explain the results of the graph neural network.