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Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs).

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Summary
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Inductive Miner, a new process mining algorithm, outperformed existing methods in analyzing educational data from student interactions. It demonstrated superior fitness and interpretability for educational models.

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Area of Science:

  • Educational Data Mining
  • Process Mining
  • Learning Analytics

Background:

  • Process mining in education traditionally uses Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner for model discovery.
  • Existing algorithms have limitations in discovering and interpreting educational process models.

Purpose of the Study:

  • To introduce and evaluate a novel process mining algorithm, Inductive Miner, for educational data.
  • To compare the performance of Inductive Miner against established algorithms using student interaction data.

Main Methods:

  • Utilized event logs from 101 university students interacting on the Moodle 2.0 platform over one semester.
  • Preprocessed 21,629 events and applied Inductive Miner, Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner for model discovery.
  • Evaluated discovered models based on fitness, precision, simplicity, and generalization.

Main Results:

  • Inductive Miner yielded superior results, particularly in terms of model fitness, a key metric for educational model discovery.
  • The algorithm consistently outperformed others when metrics were weighted according to their importance.
  • Inductive Miner generated valid and interpretable educational process models.

Conclusions:

  • Inductive Miner represents a significant advancement in process mining for educational data analysis.
  • The algorithm's ability to produce accurate and interpretable models enhances its utility in educational research and practice.
  • Inductive Miner offers a more effective approach to understanding student learning processes through data mining.