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Learning Management Systems (LMSs) are vital for Higher Education Institutions (HEIs). Despite the interest in modeling LMS interactions with big data, the analysis process is often neglected due to its complexity and tedious nature. To address such a problem, we propose here a novel multi-scale model of learners’ and instructors’ Quality of Interaction (QoI) with LMSs across different HEI scales (e.g., university, college, department, course) and learners’ registration levels (from freshmen to seniors). The proposed model combines fuzzy logic (Fuzzy-QoI model) and the Uniform Manifold Approximation and Projection (UMAP), to derive scatter plots based on Fuzzy-QoI intermediary outputs. The approach analyzed more than 40 million Blackboard LMS logs from Khalifa University, UAE, over three academic years (2020–2023), considering pre-, post-, and Covid-19 periods. Analysis results reveal the QoI dynamics for learners and instructors by role and scale. This innovative modeling offers multi-scale insights to learners, educators, and policymakers, enhancing LMS functionality and use in HEIs, and maximizing educational efficiency and knowledge experience.

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Mutli-scale Modeling of Learners’ and Instructors’ Quality of Interaction with Learning Management Systems at Higher Education Institutions

  • 会议论文

  • 发表日期：

    2025年

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  • Abdulrahman Awad,
  • Sofia B. Dias,
  • Sofia J. Hadjileontiadou,
  • Leontios J. Hadjileontiadis

摘要

Learning Management Systems (LMSs) are vital for Higher Education Institutions (HEIs). Despite the interest in modeling LMS interactions with big data, the analysis process is often neglected due to its complexity and tedious nature. To address such a problem, we propose here a novel multi-scale model of learners’ and instructors’ Quality of Interaction (QoI) with LMSs across different HEI scales (e.g., university, college, department, course) and learners’ registration levels (from freshmen to seniors). The proposed model combines fuzzy logic (Fuzzy-QoI model) and the Uniform Manifold Approximation and Projection (UMAP), to derive scatter plots based on Fuzzy-QoI intermediary outputs. The approach analyzed more than 40 million Blackboard LMS logs from Khalifa University, UAE, over three academic years (2020–2023), considering pre-, post-, and Covid-19 periods. Analysis results reveal the QoI dynamics for learners and instructors by role and scale. This innovative modeling offers multi-scale insights to learners, educators, and policymakers, enhancing LMS functionality and use in HEIs, and maximizing educational efficiency and knowledge experience.