Adaptive Learning Model Based on Ant Colony Algorithm

Rongxia Li

doi:10.3991/ijet.v14i01.9487

Abstract

To better respond to people’s demands for multimedia learning, appropriate learn-ing paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves exist-ing learning frameworks to offer a set of knowledge paths that can cater to dif-fer7ent 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 learn-ing 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 de-termine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.

Highlights

Along with the popularization of information technology, all kinds of new knowledge show an explosive growth, and people's thirst for knowledge is unquenchable
This paper proposes an adaptive learning model based on ant colony algorithm, by which adaptive learning platform system can be achieved
An adaptive learning system based on ant colony algorithm can help teachers develop personalized courses for different students and provide appropriate learning objects for learners, which, on the one hand, can improve the learning environment of students and serve them to learn better; on the other hand, this is an improvement of the existing education system, which contributes to explore better quality education services

Summary

Introduction

Along with the popularization of information technology, all kinds of new knowledge show an explosive growth, and people's thirst for knowledge is unquenchable. The most fit happens to offering tailored courses based on the learner's practical knowledge backgrounds and preferences From this angle, online learning can be interpreted as a problem for searching for optimal paths. The context-aware e-learning system provides learners with learning content based on what they are, where the context refers to any information of situation used to characterize entities as people, sites, and physical or computing objects. These parameters are used to generate a learning path that defines the sequence of activities that the learner performs with the units in the process of online learning.

Pertinent study

Adaptive learning based on ant colony algorithm

Initialize pheromone trails

Architecture of adaptive learning system

Conclusion