Discovering Student Learning Paths: An Educational Process Mining Approach in Moodle

Supriyanto Supriyanto; Taufiq Ismail; Fariz Setyawan

doi:10.59395/ijadis.v6i3.1437

Authors

Supriyanto Supriyanto Universitas Ahmad Dahlan https://orcid.org/0000-0001-9330-0683
Taufiq Ismail Universitas Ahmad Dahlan
Fariz Setyawan Universitas Ahmad Dahlan

DOI:

https://doi.org/10.59395/ijadis.v6i3.1437

Keywords:

educational process mining, heuristic miner, inductive miner, student learning path

Abstract

E-learning platforms like Moodle are critical to modern education, with their effectiveness deeply reliant on fostering optimal student engagement. A thorough understanding of how students interact with these platforms is therefore essential for enhancing the learning experience. This study aimed to analyze student interaction patterns within Moodle by employing educational process mining techniques. The core objective was to uncover hidden behavioral patterns and gain valuable insights into the underlying learning processes. To achieve this, we utilized both heuristic miner and inductive miner algorithms to analyze Moodle's extensive event log data. The effectiveness of various student activity variants was rigorously assessed through fitness checking. This study presents a novel, integrated analytical approach combining frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining to comprehensively evaluate student learning effects in Moodle. While applying both Heuristic Miner and Inductive Miner algorithms to extensive Moodle event logs, we not only generated precise process models highlighting effective and ineffective student activity sequences but also uncovered unique challenges, such as the Inductive Miner's inability to accurately model the 'Tugas' (assignment) component's complex activity patterns. These findings offer distinct, actionable insights for refining Moodle course design and delivery, moving beyond general observations to pinpoint specific pedagogical interventions. Ultimately, our work advances the understanding of student behavior and academic performance within the Moodle ecosystem by providing a granular, data-driven methodology for optimization.

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