Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

Sawsan Salem; Obadah Al Habashneh; Omar Lasassmeh

doi:10.17485/IJST/v14i2.2149

Article

Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

VIEWS 1995
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i2.2149

Year: 2021, Volume: 14, Issue: 2, Pages: 119-130

Original Article

Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

Sawsan Salem¹, Obadah Al-Habashneh^1*, Omar Lasassmeh²

¹Dept of Information Management System, School of Business, Mutah University
²Dept of Computer Information System, Faculty of Information Technology, Mutah
University, Jordan

*Corresponding Author
Email: [email protected]

Received Date:28 November 2021, Accepted Date:02 January 2021, Published Date:18 January 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: Teachers’ performance is a key bridge to ensure successful pedagogical and educational objectives. However, the evaluation of teachers’ performance has been used to be a manual and temperamental task for school principals. This traditional context limits the teachers’ engagement to develop his/her performance as well as the principle to predict the strengths and weaknesses attached. Hence, schools’ principals need to use initiative methods to evaluate the teachers’ performance. In this study, a comparative approach was developed to evaluate the teachers’ performance aiming at avoiding the potential biased and temperamental human behaves in the teacher’s evaluation process. Methods: It involves different Data Mining (DM) techniques to identify the key patterns that are driving the teachers’ performance evaluation process. Therefore, the proposed approach extracts several potential and influential indicators mined from a paper-based on teachers’ performance reports at the Directorate of Education/ Southern Ghawrs, along with some demographics variables. Several DM algorithms are used to analyze teachers’ performance reports and predict their performance, such as NB Tree, Naïve Bayes, and Conjunctive Rule methods. Findings: The experimental results show a significant prediction accuracy improvement by (33%) when applying NB Tree compared to Conjunctive rule, and (12%) when compared to Naïve Bayes techniques respectively.

Keywords: Data mining; machine learning; teachers’ performance; evaluation reports; Jordan

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Copyright

© 2021 Salem et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)