Adaptive learning model based on ant colony algorithm

Abstract

To better respond to people's demands for multimedia learning, appropriate learning paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves existing learning frameworks to offer a set of knowledge paths that can cater to differ7ent preferences, tastes, and knowledge levels of learners, no need for them to be aware of this. Based on the improved ant colony algorithm, an adaptive learning system model that can satisfy learners' demands is built herein with reference to the foraging approach of ants to traverse the paths, thereby to find the best learning path, while the classification method for some learning objects can determine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.