Kamal Chawla

Assistant Professor of Education and Applied Quantitative Methods
kamal.chawla@maine.edu
Phone: 207.581.2468

Bio: Dr. Kamal Chawla is an Assistant Professor of Education and Applied Quantitative Methods in the University of Maine College of Education and Human Development’s School of Learning and Teaching. He works at the intersection of machine learning, missing data and meta-analysis, and is dedicated to leveraging advanced quantitative methods to address critical challenges in education. Dr. Chawla’s research agenda is twofold: methodologically, he focuses on developing and refining research methods through data science and machine learning techniques to produce robust and unbiased outcomes. On the applied side, his research is centered on enhancing student learning in elementary and secondary classrooms by creating teaching strategies that are not only effective but also tailored to the diverse needs of individual students. By integrating these cutting-edge techniques, Dr. Chawla’s work aims to bridge educational gaps, empower students from all backgrounds, and contribute to a more equitable and prosperous society.

Dr. Chawla has extensively explored the intersection of meta-analysis and missing data, utilizing machine learning approaches to navigate and resolve complexities within diverse datasets. His vision for the future is to cultivate inclusive educational environments where every student feels acknowledged and empowered. With a deep awareness of cross-cultural perspectives, Dr. Chawla approaches teaching by carefully listening to his students and valuing their diverse experiences. He believes that embracing and leveraging this diversity can collectively expand the boundaries of knowledge and innovation. His commitment is to an educational future where diversity is celebrated as a powerful catalyst for change and growth, ensuring that educational systems reflect and respect the rich tapestry of human experience.

Beyond his academic pursuits, Dr. Chawla enjoys traveling, cooking, and exploring new cultures, further enriching his understanding of diversity. If you are interested in collaborating with Dr. Chawla, please feel free to email him.

Education

Courses taught at UMaine

• EHD 690: Topics in Education – Doctoral Proseminar
• EHD 573: Statistical Methods in Education I

Sample publications

Collier, Z., Chawla, K., & Soyoye, O. (2024). Optimizing Imputation for Educational Data: Exploring Training Partition and Missing Data Ratios. Journal of Experimental Education, https://doi-org.udel.idm.oclc.org/10.1080/00220973.2023.2287447

Collier, Z., Kong, M., Soyoye, O., Chawla, K., Aviles, A., & Payne, Y. (2023). Deep learning imputation for unbalanced and incomplete likert-type items. Journal of Education and Behavioral Statistics, https://doi.org/10.3102/10769986231176014

Barbieri, C.A., Miller-Cotto, D., Clerjuste, S., & Chawla, K. (2023). A meta-analysis of the worked examples effect on mathematics performance. Educational Psychology Review, 35(1), 11-43. http://dx.doi.org/10.1007/s10648-023-09745-1

Barbieri, C.A., Booth, J.L., & Chawla, K. (2022). Let’s be rational: Worked examples supplemented textbooks improve pre-algebra students’ conceptual and fraction magnitude knowledge. Educational Psychology. 1-21. https://doi.org/10.1080/01443410.2022.2144142

Chawla, K., Barbieri, C., Clerjuste, S., & Miller-Cotto, D. (preprint). How to handle clustering in meta-analysis: Theoretical and Practical Considerations. https://doi.org/10.17605/OSF.IO/8NB7P

Chawla, K., Soyoye, O., & Collier, Z. (preprint). Multiple imputation with artificial intelligence: missing data in propensity score analysis. https://doi.org/10.17605/OSF.IO/FHYMP

Chawla, K., Ali, U., & van Rijn, P. (preprint). Modern techniques for the treatment of missing responses in large-scale survey assessments. https://doi.org/10.17605/OSF.IO/GJRT3

Connect with Dr. Chawla on ResearchGate