Guided Retrieve through the K-Nearest Neighbors Method in Adaptive Learning System using the Dynamic Case Based Reasoning Approach

Abstract

The goal of adaptive learning systems is to find ways to adapt learning. There are already adaptation techniques that relate to presentation, content and navigation, but they do not make it possible to dynamically create a personalized path and to carry out an individualized follow-up of each learner by reducing the risk of failure and abandonment. We propose architecture of an adaptive learning system based on Incremental Dynamic Case Based Reasoning to provide a personalized real-time learning according to the profile of each learner and the experiences of other learners and on the K-Nearest Neighbors method to facilitate the research and classification of learners with similar behaviors, as well as to predict future behaviors.