Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/28822
Title: Integration of Data Mining Clustering Approach in the Personalized E-Learning System
Authors: Kausar, Samina
Xu Huahu
HUSSAIN, Iftikhar 
Zhu Wenhao
Zahid, Misha
Issue Date: 2018
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Source: IEEE ACCESS, 6, p. 72724-72734
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 involving 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, the personalized e-learning system architecture is presented, which detects and responds to teaching contents according to the students' learning capabilities. The primary objective includes the discovery of optimal settings, in which the 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 (CFSEDP-HD) are analyzed using educational data mining. It has been observed that more robust results can be achieved by the replacement of existing methods with CFSEDP-HD. The data mining techniques are equally effective in analyzing the big data to make education systems vigorous.
Notes: [Kausar, Samina; Xu Huahu; Zhu Wenhao] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. [Kausar, Samina] Univ Kotli Azad Jammu & Kashmir, Dept CS&IT, Kotli Azad Kashmir 11100, Pakistan. [Hussain, Iftikhar] Hasselt Univ, Inst Voor Mobiliteit IMOB, B-3500 Hasselt, Belgium. [Hussain, Iftikhar; Zahid, Misha] Beaconhouse Natl Univ, Sch Comp & IT, Terogil Campus, Lahore 53700, Pakistan.
Keywords: Big data; clustering; data-mining; educational data-mining; e-learning; profile learning;Big data; clustering; data-mining; educational data-mining; e-learning; profile learning
Document URI: http://hdl.handle.net/1942/28822
ISSN: 2169-3536
e-ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2882240
ISI #: 000454058200001
Rights: 2018 IEEE
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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