@inproceedings{1af33f842e4a4bdeb83e4cb10dbfe7f5,
title = "Investigating the Connection Between Teachers' Factors and Students' Performance in Mathematics: A UAE Case Study",
abstract = "Without a doubt, teachers play a vital role in building the students' educational foundation and critical thinking behavior, which could be quantified through various factors, especially if the taught course falls within the category of Science, Technology, Engineering and Mathematics (STEM). Models based on Machine Learning (ML) and Artificial Intelligence (AI) have proven their accuracy in predicting students' performance. However, the analyses and explanations related to the correlation strength between the students' Mathematics proficiency and the teachers' factors are not a direct task. This article presents an investigation on the connection between the various teachers' factors and students' scores in Mathematics in middle schools located in the United Arab Emirates (UAE). This type of investigation has not yet been subject in the relevant body of work in the literature so far. The study employs an AI-based framework to predict the Mathematics' scores of eighth graders and identify the significant teachers' factors using a dataset which considers 1,203 teachers with 387 features obtained from the questionnaires' responses that are issued as part of the 2019 assessment cycle of the Trends in International Mathematics and Science Study (TIMSS) report. In the proposed framework, the Extreme Gradient Boosting (XGBoost) regression model is introduced to predict students' scores in Mathematics, in conjunction with the SHapley Additive exPlanations (SHAP) algorithm to identify the significant teachers' factors. Results show that there exists a solid correlation between students' proficiency in Mathematics and factors such as job satisfaction in the education profession, observations from the teachers on students' nutrition status, in addition to the students' level of comprehension with regards to the spoken language. The developed investigation could be employed in the advancement of the educational program in the UAE in the middle-school division in specific, and the STEM-based education at large.",
keywords = "Explainable AI, machine learning, quality education, STEM, students, teachers",
author = "Maisam Wahbah and Tamador Alkhidir and Yasmin Halawani",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023 ; Conference date: 18-10-2023 Through 21-10-2023",
year = "2023",
doi = "10.1109/FIE58773.2023.10343036",
language = "British English",
series = "Proceedings - Frontiers in Education Conference, FIE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE Frontiers in Education Conference, FIE 2023 - Proceedings",
address = "United States",
}