Performance Based Adaptive Personalized eLearning System
Swati Shekapure1, Dipti D. Patil2 

1Swati Shekapure, Assistant Professor at MMCOE, Pune, India.
2Dipti D. Patil, Associate Professor at Department of Information Technology, MKSSS’s Cummins College of Engineering for Women, Pune, India.

Manuscript received on 19 March 2019 | Revised Manuscript received on 24 March 2019 | Manuscript published on 30 July 2019 | PP: 5988-5991 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3696078219/19©BEIESP | DOI: 10.35940/ijrte.B3696.078219
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Abstract: Step by step eLearning is developing pattern in industry. To the extent, learning technique is concerned it has been seen that conventional learning strategy, for example, instructor and learner as well as chalk and duster swings too inventive learning. Because of innovation in technology each one started learning by utilizing web. If learner is asking for particular learning material sometimes they are not getting relevant result. So there is need to acquire certain data of learner. This data incorporates their learning style, foundation learning, Knowledge level, learning interest, age and so forth. This proposed system tends to use retrieve, reuse, revise and retain phases of CBR. For construction of customized eLearning there has been identification of various list of features. In light of list of features there has been task of assignment of priorities according to need of it. Before retrieval process standardization of features set process is carried out. Job of K-nearest neighbour strategy to recognize impeccable k factor for better examination. Because of dynamically incremental dataset this work identifies which classification algorithm has more suitable for the dataset. Eventually eLearning saves time, enhance learning experience and provides academic success.
Index Terms: Adaptive Learning, Case Based Reasoning, K Nearest Neighbor, Learning Style

Scope of the Article: E-Learning