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Artificial intelligence techniques to predict the performance of teachers for kindergarten: Iraq as a case study

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Abstract

The teacher of a kindergarten is a paramount factor that affects the child’s future and the educational process as all. The major objective of the work is to build a model for predicting the performance of the Iraqi kindergarten teachers by using AI techniques and providing feedback for kindergartens’ teachers by determining the important teachers’ attributes using feature selection methods. The proposed work contained three major stages: the data preparation, the feature selection stage, and finally, the classification stage. The dataset has been collected by the questionnaire, the number of samples was 1450 samples of teachers from different cities in Iraq which were selected randomly, while the number of features was twenty-six. Two types of feature selection techniques utilized were Chi-square and classification and regression Tree (CART) methods. Three techniques had been used in the classification stage Support Vector Machine (SVM), Naïve Bayes (NB), and Deep Neural Network (DNN). The results showed the features had values differently in importance. The features selection technique had a positive effect on the performance, where the accuracy was 91.7%, 99.31%, and 99.68% when used NB, SVM, and DNN consecutively when using the CART selection method and 75.3%, 75.4%, and 98.7% consecutively for all features.

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References

  1. Mahgoub Y (2014) Development of teacher performance and its impact on enhancing on the quality of the educational process. Pensee 76(February):169

    Google Scholar 

  2. Asanbe MO, Osofisan AO, William WF (2016) Teachers’ performance evaluation in higher educational institution using data mining technique. Int J Appl Inf Syst (IJAIS) 10(7):10–15

    Article  Google Scholar 

  3. Baker E, Barton PE, Haertel E, Helen F (2010) Problems with the use of student test scores to evaluate teachers. Econ Policy 278(278):29

    Google Scholar 

  4. R. M. Klassen and L. E. Kim, “Selecting Teachers and Prospective Teachers : A Meta- Analysis,” no. August, 2018.

  5. Abu-Doleh J, Weir D (2007) Dimensions of performance appraisal system in Jordanian private and public organizations. Int J Hum Resour Manag 18(1):75–84

    Article  Google Scholar 

  6. Mellit A, Kalogirou SA, Hontoria L, Shaari S (2009) Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew Sustain Energy Rev 13(2):406–419

    Article  CAS  Google Scholar 

  7. Bunker RP, Thabtah F (2019) A machine learning framework for sport result prediction. Appl Comput Informatics 15(1):27–33

    Article  Google Scholar 

  8. B. Sekeroglu, K. Dimililer, K. Tuncal (2019) Student performance prediction and classification using machine learning algorithms. ACM Int Conf Proceeding Ser Part F1481: 7–11

  9. Gegic E, Isakovic B, Keco D, Masetic Z, Kevric J (2019) Car price prediction using machine learning techniques. TEM J 8(1):113–118

    Google Scholar 

  10. Vasumathi MT, Kamarasan M (2019) Fruit disease prediction using machine learning over big data. Int J Recent Technol Eng 7(6):556–559

    Google Scholar 

  11. Zoroub MK, Maghari AY (2017) Candidate teacher performance prediction using classification techniques: a case study of high schools in Gaza-strip. In: Proceedings-2017 international conference on promising electronic technology ICPET 2017. pp. 129–134

  12. Hemaid RK, Halees AME (2015) Improving teacher performance using data mining. Int J Adv Res Comput Commu Eng 4(2):407–412

    Article  Google Scholar 

  13. Naeem H, Sang J, Abro WA, Naeem MR (2015) A generic feedback system for better evaluation of teacher performance. In: Proceedings IEEE international conference software engineering service science ICSESS, vol 2015-November, no April 2017, pp 432–436

  14. Xu J, Pei L (2019) Research of teacher’s performance evaluation model based on AHP and improved PSO-BP neural network. DEStech Trans Comput Sci Eng 31:138–143

    Google Scholar 

  15. Stai E, Kafetzoglou S, Tsiropoulou EE, Papavassiliou S (2018) A holistic approach for personalization, relevance feedback & recommendation in enriched multimedia content. Multimed Tools Appl 77(1):283–326

    Article  Google Scholar 

  16. Carlson K, KM (2020) Predicting and Perceiving Teacher Effectiveness of Novice Teachers. Dissertation. Minnesota State University Moorhead, Minnesota, Moorhead

  17. Leclercq M et al (2019) Large-scale automatic feature selection for biomarker discovery in high-dimensional omics data. Front Genet 10:452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  19. Venkatesh B, Anuradha J (2019) A review of feature selection and its methods. Cybern Inf Technol 19(1):3–26

    MathSciNet  Google Scholar 

  20. Jović A, Brkić K, Bogunović N (2015) A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1200–1205. https://doi.org/10.1109/MIPRO.2015.7160458

  21. Semkow TM et al (2019) Chi-square distribution: new derivations and environmental application. J Appl Math Phys 07(08):1786–1799

    Article  Google Scholar 

  22. Franke TM, Ho T, Christie CA (2012) The chi-square test: often used and more often misinterpreted. Am J Eval 33(3):448–458

    Article  Google Scholar 

  23. Bar-hen A et al (2021) Spatial CART classification trees to cite this version : HAL Id : Hal-01837065 spatial CART classification trees

  24. Rahmadani S, Dongoran A, Zarlis M (2018) Comparison of naive bayes and decision tree on feature selection using genetic algorithm for classification problem. J Phys Conf Ser 978(1):012087

    Article  Google Scholar 

  25. Ashari A (2013) Performance comparison between naïve bayes, decision tree and k-nearest neighbor in searching alternative design in an energy simulation tool. Int J Adv Comput Sci Appl (IJACSA) 4(11):33–39

    Google Scholar 

  26. Karim M, Rahman RM (2013) Decision tree and naive bayes algorithm for classification and generation of actionable knowledge for direct marketing. J Softw Eng Appl 06(04):196–206

    Article  Google Scholar 

  27. Tsiligaridis J (2018) Classification with neural network and SVM via decision tree algorithm. AIP conference proceedings, 1982(July)

  28. Vora DR, Rajamani K (2019) A hybrid classification model for prediction of academic performance of students: a big data application. Evol Intel. https://doi.org/10.1007/s12065-019-00303-9

  29. Singh SP, Jaiswal UC (2020) Classification of audio signals using SVM-WOA in Hadoop map-reduce framework. SN Appl Sci 2(12):1–22

    Article  Google Scholar 

  30. Han K, Yu D, Tashev I (2014) Speech emotion recognition using deep neural network and extreme earning machine. In: Proceedings of annual conference on inter speech communication and association INTERSPEECH, no. September, pp 223–227

  31. Huang XL, Ma X, Hu F (2018) Editorial: machine learning and intelligent communications. Mobile Netw Appl 23(1):68–70. https://doi.org/10.1007/s11036-017-0962-2

    Article  Google Scholar 

  32. Kumar S, Chong I (2018) Correlation analysis to identify the effective data in machine learning: prediction of depressive disorder and emotion states. Int J Environ Res Public Health 15(12):2907. https://doi.org/10.3390/ijerph15122907

    Article  PubMed  PubMed Central  Google Scholar 

  33. Asskali S (2017) Polyp detection: effect of early and late feature fusion. Thesis, UNIVERSITY OF OSLO. http://www.duo.uio.no/

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Authors and Affiliations

  1. Computer Department, College of Education for Women, University of Baghdad, Baghdad, Iraq

    Rasha H. Ali

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  1. Rasha H. Ali
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Correspondence to Rasha H. Ali.

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Ali, R.H. Artificial intelligence techniques to predict the performance of teachers for kindergarten: Iraq as a case study. Evol. Intel. 17, 313–325 (2024). https://doi.org/10.1007/s12065-022-00731-0

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  • DOI: https://doi.org/10.1007/s12065-022-00731-0

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