Employing a Process Mining Approach to Recommend Personalized Adaptive Learning Paths in Blended-Learning Environments

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Abstract
In education, e-learning is highly adopted to improve the learning experience and increase learning efficiency and engagement. Yet, an explosion of online learning materials has overwhelmed learners, especially when trying to achieve their learning goals. In this scope, recommender systems are used to guide learners in their learning process by filtering out the available resources to best match their needs, i.e. to offer personalized content and learning paths. Concurrently, process mining has emerged as a valuable tool for comprehending learner behavior during the learning journey. To synergize these disciplines and optimize learning outcomes, our paper introduces an ontology-based framework that aims to recommend an adaptive learning path, driven by a learner’s learning objective, personalized to his learning style, and enriched by the past learning experience of other learners extracted via process mining. The learning path considers pedagogical standards by employing Bloom’s taxonomy within its structure. The framework establishes an Ontological Foundation, to model the Learner, Domain, and Learning Path. Choosing Computer Science as a domain, we construct a knowledge base using synthesized data. For past learning experience, we analyze Moodle log data from 2018 to 2022, encompassing 471 students in the Computer Science and Engineering Department at Frederick University, Cyprus.

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