Abstract:

The evolution of digital technologies has brought significant advancements in the field of education, particularly with the emergence of online learning systems. Among innovative approaches, adaptive content stands out as a promising method to enhance personalization and inclusivity in virtual learning environments. By leveraging artificial intelligence (AI) and machine learning, this approach aims to provide tailored learning paths that cater to the specific needs of each learner while promoting equitable education for all. This article explores the principles of adaptive content and its role in enhancing the efficiency and accessibility of online learning.

To assess the effectiveness of adaptive content, we collected data related to students' academic performance, interactions with educational content, as well as evaluations and feedback. To preserve participant confidentiality, all data were anonymized and handled in compliance with data protection protocols.
In this study, we utilized the K-nearest neighbors (KNN) classification algorithm to analyze the data and create individual learning profiles for each student. These profiles were developed by considering each learner's skills, prior knowledge, and preferences. Leveraging these profiles, the online learning platform could offer tailored educational content to each student, providing a more personalized learning experience.
A widely used algorithm for implementing adaptive content is collaborative filtering based on recommendation systems, particularly the Memory-Based Collaborative Filtering algorithm. This approach relies on user interactions with educational content to identify relevant elements and recommend them to learners in a targeted manner.

The results of this study demonstrated that adaptive content has a significant impact on the effectiveness of online learning. Learners who benefited from personalized learning paths tailored to their individual needs showed a notable improvement in knowledge retention. By receiving relevant and targeted educational content, learners exhibited higher engagement and motivation, positively influencing their academic performance.
Moreover, adaptive content proved to be a promising approach to promote inclusivity in online education. By addressing the specific needs of learners, such as diverse learning styles or disabilities, adaptive content facilitated greater accessibility for all. This contributed to reducing disparities and offering an equitable educational experience for every learner, regardless of their background.

Keywords:

Adaptive content, Personalization, Inclusivity, K-nearest neighbors, Collaborative filtering.