Publications – Machine Learning
Leite, W. L., Roy, S., Chakraborty, N., Michailidis, G., Huggins-Manley, A. C., D’Mello, S. K., Faradonbeh, M. K. S., Jensen, E., Kuang, H. & Jing, Z. (2022). A novel video recommendation system for algebra: An effectiveness evaluation study. In LAK22: 12th International Learning Analytics and Knowledge Conference (LAK22), March 21–25, 2022, Online, USA. ACM, New York, NY, USA, https://doi.org/10.1145/3506860.3506906
Chakraborty, N., Roy, S., Leite, W. L., Faradonbeh, M. K. S., & Michailidis, G. (2021). The effects of a personalized recommendation system on students’ high-stakes achievement scores: A field experiment. The 14th Conference on Education Data Mining, June 29th – July 2nd Paris, France.
Shin, J., Balyan, R., Banawan, M., Leite, W. L., McNamara, D. (upcoming). Combining Discourse Analysis and Theory-driven Natural Language Processing: Revealing Distinct Patterns of Pedagogical Communication in Algebra Tutoring.
Shin, J., Balyan, R., Banawan, M., Leite, W. L., McNamara, D. (2021). Pedagogical Communication Language in Video Lectures: Empirical Findings from Algebra Nation. 2021 Meeting of the International Society of Learning Sciences. https://repository.isls.org/bitstream/1/7484/1/323-329.pdf
Banawan, M., Balyan, R., Shin, J., Leite, W. L., McNamara, D. (2021). Linguistic Features of Discourse within an Algebra Online Discussion Board. The 14th Conference on Education Data Mining, June 29th – July 2nd Paris, France. https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_89.pdf
Li, C., Xing, W., & Leite, W. L. (2021). Yet Another Predictive Model? Fair Predictions of Students’ Learning Outcomes in an Online Math Learning Platform. Proceedings of the 11th International Learning Analytics and Knowledge Conference (LAK21). https://doi.org/10.1145/3448139.3448200
Li C., Xing W., Leite W. (2021) Using Fair AI with Debiased Network Embeddings to Support Help Seeking in an Online Math Learning Platform. In: Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_44
Collier, Z. K., & Leite, W. L. (2021). A Tutorial on Artificial Neural Networks in Propensity Score Analysis. Journal of Experimental Education. DOI: 10.1080/00220973.2020.1854158
Collier, Z. K., Leite, W. L, & Zhang, H. (2021): Estimating propensity scores using neural networks and traditional methods: a comparative
simulation study, Communications in Statistics – Simulation and Computation, DOI: 10.1080/03610918.2021.1963455
Collier Z. K., Leite W. L., Karpyn A. (2021). Neural Networks to Estimate Generalized Propensity Scores for Continuous Treatment Doses. Evaluation Review. doi:10.1177/0193841X21992199
Leite, W. L., Shen, Z., Marcoulides, K. M., Fisk, C., & Harring, J. (2021). Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling. Structural Equation Modeling. 29, 1, 47-56. https://doi.org/10.1080/10705511.2021.1881786
Collier, Z. K. & Leite, W. L. (2017). A Comparison of Three-Step Approaches for Auxiliary Variables in Latent Class and Latent Profile Analysis. Structural Equation Modeling, 24:6, 819-830, DOI: 10.1080/10705511.2017.1365304
Hu, J., & Leite, W. L. (2017). An Evaluation of the Use of Covariates to Assist in Class Enumeration in Linear Growth Mixture Modeling. Behavior Research Methods, 49 (3), 1179-1190.
Xue, K., Huggins-Manley, A. C., & Leite, W. L. (2020). Semi-supervised Learning Method for Adjusting Biased Item Difficulty Estimates Caused by Nonignorable Missingness under 2PL-IRT Model. In: A. N. Rafferty, J. Whitehill, C. Romero, & V. Cavalli-Sforza (eds). Proceedings of The 13th Conference of Educational Data Mining. https://educationaldatamining.org/files/conferences/EDM2020/papers/paper_217.pdf
Raborn, A., Leite, W., and Marcoulides, K. (2019). A Comparison of Automated Scale Short Form Selection Strategies. In: M. Desmarais, C. F. Lynch, A. Merceron, & R. Nkambou (eds.) The 12th International Conference on Educational Data Mining, pp. 402 – 407
Rarborn, A. W. , & Leite, W. L. (2018). ShortForm: An R package to select scale short forms with the ant colony optimization algorithm. Applied Psychological Measurement, 42(6), 516-517. https://doi.org/10.1177/0146621617752993
Leite, W. L., & Cooper, L. A. (2010). Detecting social desirability bias using factor mixture models. Multivariate Behavioral Research, 45, 271-293.
Leite, W. L., Huang, I., & Marcoulides, G. A. (2008). Item selection for the development of short-forms of scales using an Ant Colony Optimization algorithm. Multivariate Behavioral Research.43, 411-431.
