An Adaptive Learning System Based on Ant Colony Algorithm
Abhishek Kumar1, J.E. Nalavade2, Vinay Yeola3, Vishal Vivek4, Yatharth Srivastava5
1Abhishek Kumar, Computer engineering, University of Pune/ SIT, Pune
2J.E. Nalavade, Computer engineering, University of Pune/ SIT, Pune.
3Vinay Yeola, Computer engineering, University of Pune/ SIT, Pune, India.
4Vishal Vivek, Computer engineering, University of Pune/ SIT, Pune, India.
5Yatharth Srivastava, Computer engineering, University of Pune/ SIT, Pune, India.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 212-214 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1502053213
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Adaptive learning, ant colony, learning object, learning style, learner.