Abstract:

Online learning platforms have notably revolutionized the modern educational landscape, catering specifically to the nuanced needs of learners. At the forefront of this transformation is the ASRIT Academy, an advanced Learning Management System (LMS) tailored for the members of the Association of Scientific Research, Innovation, and Technology based in Tetuan, Morocco. Integral to ASRIT Academy's success is its content recommendation algorithm, which is geared towards optimizing and personalizing the educational experiences of its users.
The study embarks with a detailed introduction of ASRIT Academy, positioning it as a paragon in the realm of e-learning. It subsequently delves into the algorithm's intricacies, emphasizing its alignment with pedagogical principles and its potential to reshape teaching and learning dynamics.
At the heart of the research lies a comprehensive empirical analysis, aiming to ascertain how the algorithm influences and enhances the academic journey on ASRIT Academy. Harnessing methods such as learner-centric surveys, platform analytics underlining educational engagement, and A/B testing contrasting varied pedagogical approaches, the study unveils profound insights into the algorithm’s educational potential.
Findings depict that the algorithm fosters robust academic engagement. Learners exposed to its recommendations demonstrate prolonged engagement durations, augmented platform usage, and a deepened learning commitment. Crucially, feedback elucidates heightened learner satisfaction, largely attributed to the relevance and personalization of content orchestrated by the algorithm.
Nevertheless, the educational domain presents inherent challenges. The algorithm, while proficient in curating personalized content, may inadvertently confine learners to a constrained knowledge framework, potentially hampering comprehensive learning and exposure to diverse academic perspectives. A palpable desire amongst users also emerges, advocating for greater transparency in the algorithm's modus operandi and its content selection rationale.
In the study accentuates the imperatives in e-learning: crafting a fine equilibrium between content personalization and diversified knowledge acquisition, upholding algorithmic transparency, and preserving the sanctity of the learning milieu by minimizing disruptive ad interferences. These findings and recommendations are pivotal not just for ASRIT Academy, but broadly for e-learning platforms endeavoring to enhance their pedagogical methodologies.

Keywords:

Recommendation system, Personalized learning, Learning styles, e-learning, e-learning system.