Associate Professor in Mathematics, Faculty of Mathematics and Data Science
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Dr. Thomas Mgonja
Prof. Dr. Zindoga Mukandavire
Prof. Dr. Kaitano Dube
Dr. Ahlam Mohammed Alzoubi
Dr. Nidhi Chaturvedi
Dr. Zara Canbary
Dr. Evangelia Pantelaki
Dr. Wasim Ahmad
Dr. Sevda Ahmadian
Dr. Crystal Ioannou
Dr. Annamalai Chockalingam
Ronak J Lad
Elif Ranclaud
Dr. Muneer Jahwash
Dr. Petr Svoboda
Dr. Bhavana Rajeev
Dr. Baha Mohsen
Dr. Georgina Farouqa
Prof. Dr. Hicham Machmouchi
Omar Chafic
Dr. Elham Tolouei
Dr. Anju Anna Jacob
Dr. Walid Abou Hweij
Eng. Ajit Yesodharan
Eng. Manuel Abong
Eng. Shirley Fernandes
Mohamed Zouhir
Dr. Afaq Altaf
Dr. Ehsaneh Essen Etemadi
Prof. Hannah Al Ali
Prof. Alireza Daneshkhah
Dr. Mostafa Kamil
Dr. Muner Mustafa Abou Hasan
Dr. Blessy Trencia Lincy
Dr. Deepudev Shahadevan
Dr. Mohammad Abu Zaytoon
Dr. Rfaat Soliby
Dr. Zainab Rasheed
Dr. Rukshanda Kamran
Mawada Nasser
Riham Arab
Dr. Mahmoud Alkhouli
Prof. Dr. Daoud Hilal
Associate Professor in Mathematics, Faculty of Mathematics and Data Science
Dr. Thomas Mgonja is an Associate Professor at the Faculty of Mathematics and Data Science. He earned his bachelor’s degree in Mathematics from Idaho State University, a master’s degree in Mathematics with an emphasis in financial mathematics from Florida State University, and a Ph.D. in Mathematics Education from Utah State University. Dr. Mgonja has been in academia since 2010, focusing his research on mathematical and machine learning modeling with education and agroeconomic data. He is an active member of several mathematics and mathematics education associations and is deeply passionate about both his research and teaching. When he’s not working or having in a nerdy moment, he’s likely spending time with family, working out, or watching American football.
Ph.D. in Mathematics Education
Masters in Mathematics
Bachelors in Mathematics
Machine learning
Mathematics
Statistics
Machine Learning Modelling for education and agroeconomic data
Cultural Relevant Pedagogy for mathematics
Mgonja, T. A., (2023) Using Interpretable Machine Learning Approaches to Predict and Provide Explanations for Student Completion of Remedial Mathematics, Education and Information Technology, 25710259241383214
Mgonja, T. A., Moyer-Packenham, P., & Marx, S., (2022). Assessing the Impact of Culturally Relevant Pedagogy on Students’ Mathematics Performance, Journal of Diversity in Higher Education, 15210251221083314.
Mgonja, T., & Robles, F. (2022). Using Machine Learning Techniques to Identify Critical Factors When Predicting Remedial Mathematics Completion Rates. Journal of College Student Retention: Research, Theory & Practice, 15210251221083314.
Mgonja, T. A., "Examining the Use of Culturally Relevant Pedagogy in Undergraduate Mathematics Learning Modules with Students of Color" (2021). All Graduate Theses and Dissertations. 8137. https://digitalcommons.usu.edu/etd/8137
Mgonja, T. A. Marx, S., & Moyer-Packenham, P. (2021). A Review of Culturally Relevant Pedagogy for Students of Color in Mathematics, Multicultural Education, 7(6), 422-432.
McKenna, H., Chang K. L., & Mgonja, T. A., (2021). A Framework to Measure Microaggressions in the Mathematics Classroom. Submitted to SN Social Sciences, 1(5), 1-21.
Mgonja, T. A. (2020, November). The Lost Voices in Mathematics Teaching, Utah Council of Teachers of Mathematics, 50, 50-62.
Mgonja, T. A. & Chang, K.L. (2019, January). Difficulties in solving linear equations with fractions, Utah Council of Teachers of Mathematics, 49, 13-29.
Mgonja, T. A., Chang K.L., & McKenna, H. (2017, February). Bridging the gaps between teachers’ and students’ perspectives of a culturally inclusive classroom, Proceedings of the Research in Undergraduate Mathematics Education (pp.1645-1646), San Diego, California.
Chang, K.L., McKenna, H., & Mgonja, T.A. (2017, February). Diagrams for the reasoning and proof of amortization formula, Proceedings of the Research in Undergraduate Mathematics Education (pp.1551-1552), San Diego, California.