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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier

Author 1: Lalbihari Barik
Author 2: Ahmad AbdulQadir AlRababah
Author 3: Yasser Difulah Al-Otaibi

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110371

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.

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Abstract: The implementation of computer-supported collaborative learning has come to play a pivotal role in e-learning platforms. Educational Data Mining (EDM) is a promising area for the exclusive skill development of e-learning tutors, the major concern being investigations over large datasets. The tutors possessing efficient and sufficient soft skills can teach students within less time and with greater productivity. EDM is a regularly used research area that handles the development of methods to explore new ideas in the educational field. Computer-supported collaborative learning in e-learning and competencies on a real-time perspective among teachers are calculated using statistical classifiers. This paper aims to identify a feasible perspective on EDM based ICT competency over e-learning tutors using statistical classifiers. A set of tutors from diverse e-learning centers of various universities is selected for the evaluation purpose. The teachers from the department of mathematics in the universities are selected to attend a professional Qualified Teacher Status numeracy skills test and tutors’ online test. The results of online tests are collected and correlated with the Naive Bayes Classifiers algorithms. Naive Bayes Classifiers are used in this paper to find the classification performance results among teachers. Naive Bayes based classification is beneficial for skill identification and improvement among the teachers. Significantly, the data mining classifiers performed well with the large dataset.

Keywords: Data mining; e-learning tutors; Naive Bayes Classifiers algorithms; ICT; QTS numeracy

Lalbihari Barik, Ahmad AbdulQadir AlRababah and Yasser Difulah Al-Otaibi, “Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110371

@article{Barik2020,
title = {Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110371},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110371},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Lalbihari Barik and Ahmad AbdulQadir AlRababah and Yasser Difulah Al-Otaibi}
}


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