文章基本信息

标题：An Adaptive Learning System Based on Ant Colony Algorithm
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作者：Abhishek Kumar ; J.E. Nalavade ; Vinay Yeola 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2013
卷号：3
期号：2
页码：212-214
出版社：International Journal of Soft Computing & Engineering
摘要：One of the most important emerging requirements of the learning is adaptation to learner’s needs. Adaptive learning will permit improvements in the current scenario. It suggests courses adapted to results, behaviors, preferences, tastes of learners. In the present paper, we have proposed an approach based on the Ants colonies' optimization algorithm. This helps to recommend a learning course. It adapts to fit in the best manner into learner's profiles. The approach is helpful in improving both the learning achievement and learning efficiency of individual Learners. Learners with different attributes may locate learning objects (LO) which have a higher probability of being suitable. A web-based learning approach was created for learners to find the learning objects more effectively. We propose an attribute based ant colony system to help learners find an adaptive LO more effectively.
关键词：adaptive learning; ant colony; learning object;learning style; learner