Preprint Article Version 2 This version is not peer-reviewed

Integration of Data Mining Clustering Approach with the Personalized E-Learning System

Version 1 : Received: 19 August 2018 / Approved: 20 August 2018 / Online: 20 August 2018 (10:38:27 CEST)
Version 2 : Received: 18 October 2018 / Approved: 19 October 2018 / Online: 19 October 2018 (05:58:05 CEST)

How to cite: Kausar, S.; Xu, H.; Hussain, I.; Zhu, W.; Zahid, M. Integration of Data Mining Clustering Approach with the Personalized E-Learning System. Preprints 2018, 2018080350 (doi: 10.20944/preprints201808.0350.v2). Kausar, S.; Xu, H.; Hussain, I.; Zhu, W.; Zahid, M. Integration of Data Mining Clustering Approach with the Personalized E-Learning System. Preprints 2018, 2018080350 (doi: 10.20944/preprints201808.0350.v2).

Abstract

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

Subject Areas

big data; clustering; data mining; educational data mining; e-learning; profile learning

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