School of Education
Cohort: 2020
Christina Barbieri
School of Education
Educational Statistics and Research Methods
Kamal Chawla
Kamal Chawla is a Ph.D. in Educational Statistics and Research Methods student in the School of Education at the University of Delaware. His research program focuses on the development and evaluation of sophisticated quantitative methods to solve important issues in the field of education.
He has two main lines of research: the first is applied and focuses on evaluating methods for improving mathematics learning in classrooms. The second is more methodological and focused on critical evaluation and development of educational research methods through data science and machine learning techniques to produce more robust and unbiased solutions. He believes, combinedly both areas contribute to discovering new concepts, measuring their prevalence, and further assisting in predictions in the field of education.
He received his bachelor’s degree in Mathematics from the University of Delhi, India, and his master’s degree in Industrial Mathematics and Informatics from the Indian Institute of Technology (IIT) in Roorkee, India.
Education
- B.Sc., Mathematics with Honors, University of Delhi, Delhi, India, 2013
- M.Sc., Industrial Mathematics and Informatics, Indian Institute of Technology, Roorkee, India, 2016
Professional Experience
- Graduate Research Assistant, School of Education, University of Delaware, 2020–present
- Area Manager, Quantitative Aptitude Trainer, The Princeton Review, Noida, India, 2016–2020
- Visiting Researcher, National Institute of Hydrology, Roorkee, India, 2015
- Research Assistant, Department of Mathematics, Indian Institute of Technology, 2015
- Research Assistant, Department of Mathematics, Sri Venkateswara College, University of Delhi, 2012
Publications
- 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://journals.sagepub.com/doi/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, https://link.springer.com/article/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
- Collier, Z., Chawla, K., & Soyoye, O. (accepted with minor revisions). On Imputing High Missingness with Small Data in Propensity Score Analysis.
- Chawla, K, Barbieri, C.A. & Acharya, S. (second revision). An international comparison of dimensional, contextual, and mathematical features and cognitive demands of High School Geometry texts.
- Chawla, K. & Chawla, R. (under review). IRT Score Error Estimates in Item Modeling-Analytical and Bootstrapping Methods.
- Barbieri, C. A., Clerjuste, S., Silla, E., & Chawla, K. (under review). Leveraging common mathematical errors to support core mathematics competencies
- Chawla, K. (2019). Kar Kuch Aisa. India: Notionpress.