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DOI: 10.14569/IJACSA.2019.0100835
PDF

Learning Analytics Framework for Adaptive E-learning System to Monitor the Learner’s Activities

Author 1: Salma EL Janati
Author 2: Abdelilah Maach
Author 3: Driss El Ghanami

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 8, 2019.

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Abstract: The adaptive e-learning system (AE-LS) research has long focused on the learner model and learning activities to personalize the learner’s experience. However, there are many unresolved issues that make it difficult for trainee teachers to obtain appropriate information about the learner's behavior. The evolution of the Learning Analytics (LA) offers new possibilities to solve problems of AE-LS. In this paper, we proposed a Business intelligence framework for AE-LS to monitor and manage the performance of the learner more effectively. The suggested architecture of the ALS proposes a data warehouse model that responds to these problems. It defines specifics measures and dimensions, which helps teachers and educational administrators to evaluate and analyze the learner’s activities. By analyzing these interactions, the adaptive e-learning analytic system (AE-LAS) has the potential to provide a predictive view of upcoming challenges. These predictions are used to evaluate the adaptation of the content presentation and improve the performance of the learning process.

Keywords: e-Learning; adaptive e-learning system; learner model; learning analytics; business intelligence; data warehouse; content presentation

Salma EL Janati, Abdelilah Maach and Driss El Ghanami, “Learning Analytics Framework for Adaptive E-learning System to Monitor the Learner’s Activities” International Journal of Advanced Computer Science and Applications(IJACSA), 10(8), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100835

@article{Janati2019,
title = {Learning Analytics Framework for Adaptive E-learning System to Monitor the Learner’s Activities},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100835},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100835},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {8},
author = {Salma EL Janati and Abdelilah Maach and Driss El Ghanami}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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