Adaptive Learning Model Based on Ant Colony Algorithm

Authors

  • Rongxia Li Modern International Academy of design art, Chongqing Technology and Business University

DOI:

https://doi.org/10.3991/ijet.v14i01.9487

Keywords:

Ant algorithm, adaptive learning, learning path, learning style

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.

Author Biography

Rongxia Li, Modern International Academy of design art, Chongqing Technology and Business University

Rongxia Li, female (1984.06--), Master, Modern International Academy of de-sign art, Chongqing Technology and Business University, Chongqing 401120, China. The main research areas: ideological and political education and pedagogy, E-mail: 115084138@qq.com

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Published

2019-01-17

How to Cite

Li, R. (2019). Adaptive Learning Model Based on Ant Colony Algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(01), pp. 49–57. https://doi.org/10.3991/ijet.v14i01.9487

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Section

Papers