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Abstract

The process of performance monitoring in e-learning platforms is one of the emerging techniques that enable educators to assess and improve the educational outcomes of the students. This is typically accompanied by few challenges that need to be addressed, including data privacy and security, maintaining data quality and availability, scaling performance monitoring for large numbers of learners, handling the heterogeneity of data, ensuring interpretability and explainability, considering contextual factors, addressing ethical considerations, and establishing robust technological infrastructure. Many methods and techniques are considered to resolve these issues, including the implementation of strong data privacy protection measures, applying data validation and cleaning techniques, utilizing scalable data processing and storage frameworks, using advanced analytics methods to handle diverse data types, developing interpretable machine learning models and model-agnostic techniques for explainability, incorporating contextual factors into performance monitoring models, complying with the ethical guidelines and conducting regular ethical reviews, and investing in robust technological infrastructure. In modern e-learning platforms, fuzzy rules can be applied to monitor e-learners' activities which will provide a flexible and adaptive approach to managing and guiding learners' interactions and behaviors within the platform. This paper investigates the methods and roles for optimizing learning experiences by improving the means of student monitoring.

Keywords

Data standardization, Decision trees, Ethical considerations, Ethical guidelines, Ensemble learning, Fuzzy rules, Hierarchical modeling

Subject Area

Computer Science

First Page

677

Last Page

686

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Receive Date

11-30-2023

Revise Date

5-3-2024

Accept Date

5-5-2024

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