Attributes of Low Performing Students In E-Learning System Using Clustering Technique

Authors(1) :-Ebiemi Allen Ekubo

Data mining in education is considered to be one of the relevant and fast growing areas in data mining, with free access to datasets available online, researchers have continued to analyze and produce knowledge which has improved the educational sector. With many research geared towards predicting student results, this paper offers a different approach of gaining knowledge of student data by presenting the attributes of low-performing students. The idea is to group students with low grades and discover the core attributes of these category of students, thereby providing stakeholders with these attributes which should be looked out for in current and prospective students. The dataset used in this research was collected from an e-Learning system called Kalboard 360. The k-means clustering technique embedded in the WEKA tool was used to group these category of students into two clusters. The knowledge gained from the mining process shows that lower-level absentee students with parents that do not actively participate in their learning process are most likely to perform poorly in their studies.

Authors and Affiliations

Ebiemi Allen Ekubo
North-West University, Potchefstroom, South Africa

Data Mining, E-learning, WEKA, Low-Performing Students.

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Publication Details

Published in : Volume 5 | Issue 3 | May-June 2019
Date of Publication : 2019-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 480-485
Manuscript Number : CSEIT1953158
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ebiemi Allen Ekubo, "Attributes of Low Performing Students In E-Learning System Using Clustering Technique", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.480-485, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT1953158
Journal URL : https://res.ijsrcseit.com/CSEIT1953158 Citation Detection and Elimination     |      |          | BibTeX | RIS | CSV

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