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

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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

References

Maghasbeh MKA, Khraisat HMM. Towards A Multi Agent System Based Data Mining for Proteins Prediction and Classification. J. Sci. Technol. Res. 2015;4.
Burgos C, Campanario ML, Peña Ddl, Lara JA, Lizcano D, Martínez MA. Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering. 2018;66:541–556. Available from: https://dx.doi.org/10.1016/j.compeleceng.2017.03.005
Felizardo JC, Macdonell KR, Mendes SG, Maldonado E. A systematic mapping on the use of visual data mining to support the conduct of systematic literature reviews. arXiv Prepr. arXiv2012.10833. 2020.
Liljeström M, Paulin H, Holmgren C. In search of online learning 3.0 in higher education—the role of theory-based process evaluation. In: ICICTE 2019, International Conference on Information Communication Technologies in Education. (pp. 21-30) 2019.
Awad M, Salameh K, Leiss EL. Evaluating Learning Management System Usage at a Small University. Proceedings of the 2019 3rd International Conference on Information System and Data Mining. 2019;p. 98–102.
Mohamad SK, Tasir Z. Educational Data Mining: A Review. Procedia - Social and Behavioral Sciences. 2013;97:320–324. Available from: https://dx.doi.org/10.1016/j.sbspro.2013.10.240
Ogor EN. Student academic performance monitoring and evaluation using data mining techniques. Electronics, Robotics and Automotive Mechanics Conference. 2007;p. 354–359.
Papamitsiou Z, Economides AA. Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. J. Educ. Technol. Soc. 2014;17(4):49–64.
Márquez-Vera C, Cano A, Romero C, Ventura S. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence. 2013;38(3):315–330. Available from: https://dx.doi.org/10.1007/s10489-012-0374-8
Shahiri AM, Husain W, Rashid NA. A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science. 2015;72:414–422. Available from: https://dx.doi.org/10.1016/j.procs.2015.12.157
Ali MM. Role of data mining in education sector. Int. J. Comput. Sci. Mob. Comput. 2013;2(4):374–383.
Chalaris M, Gritzalis S, Maragoudakis M, Sgouropoulou C, Tsolakidis A. Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques. Procedia - Social and Behavioral Sciences. 2014;147:390–397. Available from: https://dx.doi.org/10.1016/j.sbspro.2014.07.117
Al-Barrak MA, , Al-Razgan M, . Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology. 2016;6(7):528–533. Available from: https://dx.doi.org/10.7763/ijiet.2016.v6.745
Pal AK, Pal S. Evaluation of teacher’s performance: a data mining approach. Int. J. Comput. Sci. Mob. Comput. 2013;2(12):359–369.
Ola A, Pallaniappan S. A data mining model for evaluation of instructors’ performance in higher institutions of learning using machine learning algorithms. Int. J. Conceptions Comput. Inf. Technol. 2013;1(1).
Ahmadi F, Abadi MESA. Data Mining in Teacher Evaluation System using WEKA. International Journal of Computer Applications. 2013;63(10):12–18. Available from: https://dx.doi.org/10.5120/10501-5268
MARDIKYAN S, BADUR B. Analyzing Teaching Performance of Instructors Using Data Mining Techniques. Informatics in Education. 2011;10(2):245–257. Available from: https://dx.doi.org/10.15388/infedu.2011.17
Agaoglu M. Predicting Instructor Performance Using Data Mining Techniques in Higher Education. IEEE Access. 2016;4:2379–2387. Available from: https://dx.doi.org/10.1109/access.2016.2568756
Alom BMM, Courtney M. Educational data mining: a case study perspectives from primary to university education in australia. Int. J. Inf. Technol. Comput. Sci. 2018;10(2):1–9.
Chaware AN, Lanjewar UA. A Novel Educational Datamining Model to Attain Sustainability. Int. J. Adv. Res. Comput. Sci. 2014;5(1).
Dua S, Du X. Data mining and machine learning in cybersecurity. CRC press. 2016.
Galdi P, Tagliaferri R. Data mining: Accuracy and error measures for classification and prediction. Encycl. Bioinforma. Comput. Biol. 2018;p. 431–436.

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)