Personalizing the Learning Experience: An Adaptive Algorithm Model Based on K-NN
- DOI
- 10.2991/978-94-6463-360-3_7How to use a DOI?
- Keywords
- Adaptive Learning Systems; K-nearest neighbors (k-NN) algorithm; Content difficulty adjustment
- Abstract
This paper proposes the use of the k-NN algorithm as a method for adjusting the level of difficulty of educational content based on learner preferences and performance. The goal is to achieve personalized learning experiences that can improve learner engagement and performance. The paper provides a theoretical framework on adaptive learning systems and content difficulty adjustment, which involves using data analytics and machine learning algorithms to adapt educational content to the individual learner’s abilities and knowledge level. The k-NN algorithm is explained in detail, along with its steps and design process. The paper proposes a general design process that involves collecting data, preprocessing data, training the k-NN model, and adjusting content difficulty based on the predictions generated by the k-NN model.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hicham Er-Radi AU - Souhaib Aammou AU - Zakaria Tagdimi AU - Ikram Amzil PY - 2024 DA - 2024/02/05 TI - Personalizing the Learning Experience: An Adaptive Algorithm Model Based on K-NN BT - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023) PB - Atlantis Press SP - 50 EP - 56 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-360-3_7 DO - 10.2991/978-94-6463-360-3_7 ID - Er-Radi2024 ER -