Here, you’ll find a collection of presentations, talks, and workshops that showcase the work and ideas emerging from the Centroid Applied Machine Learning Lab. These materials reflect our commitment to advancing ethical and responsible machine learning in the social sciences and highlight our collaborative efforts to share knowledge, spark dialogue, and drive innovation. Whether you're a researcher, practitioner, or student, we invite you to explore how our work is informing practice, shaping policy, and contributing to a deeper understanding of human behavior and societal trends.
Machine Learning Papers
Bogenschutz, M., Broda, M., Dinora, P., Lineberry, S., Prohn, S., & West, A. (2024). Examining Use of Pharmacotherapy for Behavioral Support Among Americans with IDD Using Machine Learning. Journal of Mental Health Research in Intellectual Disabilities. https://doi.org/10.1080/19315864.2024.2416696
Broda, M. D., Bogenschutz, M., Dinora, P., Prohn, S., Lineberry, S., & West, A. (2024). Understanding COVID-19 infection among people with intellectual and developmental disabilities using machine learning. Disability and Health Journal, 17(3), 101607. https://doi.org/10.1016/j.dhjo.2024.101607
Broda, M. D., Bogenschutz, M., Lineberry, S., Dinora, P., Prohn, S., & West, A. (2023). Comparing employment, employment services, and employment goals in propensity-matched samples of people with intellectual and developmental disabilities with and without autism. Journal of Vocational Rehabilitation, 58(3), 307–316. https://doi.org/10.3233/jvr-230019
Broda, M. D., Bogenschutz, M., Dinora, P., Prohn, S. M., Lineberry, S., & Ross, E. (2021). Using Machine Learning to Predict Patterns of Employment and Day Program Participation. American Journal on Intellectual and Developmental Disabilities, 126(6), 477–491. https://doi.org/10.1352/1944-7558-126.6.477
Chang, C. N., Hui, J., Justus-Smith, C., & Wang, T.-W. (2024). Navigating STEM careers with AI mentors: A new IDP journey. Frontiers in Artificial Intelligence, 7, 1-16. https://doi.org/10.3389/frai.2024.1461137
Chang, C. N., Lin, S., Kwok, O., & Saw, G. K. (2023). Predicting STEM major choice: a machine learning classification and regression tree approach. Journal for STEM Education Research, 6, 358–374. https://doi.org/10.1007/s41979-023-00099-5
Chang, C. N., Chien, H. Y., & Malagon-Palacios, L. (2022). College reopening and community spread of COVID-19 in the United States. Public Health, 204, 70-75. https://doi.org/10.1016/j.puhe.2022.01.001
Additional Selected Papers
Broda, M. D., Conley, A. H., Clarke, P. B., Ohrt, J. H., & Joy, J. (2025). Examining the dimensionality, internal consistency, and invariance of the Depression, Anxiety, and Stress Scale–21 (DASS-21) across age, race, ethnicity, and caretaking status. Psychological Reports. https://doi.org/10.1177/00332941241313105
Broda, M. D., Ross, E., Sorhagen, N., & Ekholm, E. (2023). Exploring control-value motivational profiles of mathematics anxiety, self-concept, and interest in adolescents. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1140924
Chow, J. C., Broda, M. D., Granger, K. L., Washington-Nortey, M., Sayers, R., & Dunn, D. (2023). A sociometric approach to understanding characteristics of same- and other-gender friendships in young children. Early Childhood Research Quarterly, 62, 385–393. https://doi.org/10.1016/j.ecresq.2022.09.009
Corning, A., Broda, M. D., Lucas, B.-L., Becker, J. D., & Bae, C. L. (2023). An inclusive school for computer science: Evaluating early impact with propensity score matching. Studies in Educational Evaluation, 79, 101293. https://doi.org/10.1016/j.stueduc.2023.101293
Chang, C. N., & Kwok, O. (2022). Partitioning variance for a within-level predictor in multilevel models. Structural Equation Modeling: A Multidisciplinary Journal, 29(5), 716-730. https://doi.org/10.1080/10705511.2022.2051175
Greene, M. D., Broda, M., Chen, C-C., Chang, C.-N., Wang, T. W., Joy, J., & Xia, Y. (2025, April-forthcoming). Zero-shot learning and support vector machine methods for automated abstract screening in scoping reviews. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Broda, M., Joy, J., Greene, M. D., Wang, T-W., Liu, X., Chen, C-C., & Xia, Y. (2025, April- forthcoming). A scoping review of machine learning applications in education research. Poster Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Jones, W., LeVault, R., Hanson, K., Liu, X., & Greene, M. D. (2025, April-forthcoming). Teaching computer programming through digital music composition: Effects on college students’ dispositions toward computer science. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Chang, C. N., Liu, X., Xia, Y., & Wang, T. W (2025, April-forthcoming). Weighting solution: Verifying SEM trees for national data. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Chang, C. N., Chen, C. C., Corning, A., Saw, G., Liu, X., Wang, T. W., & Greene, M. D. (2025, April-forthcoming). Science identity pathways and STEM career aspirations: Intersectional analysis from high school to postsecondary education. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Xia, Y., Xu, Y., Chen, C. C., Chang, C. N., Broda, M., Liu, X., & Greene, M. D. (2025, April-forthcoming). Exploring the age sensitivity of young children's social-emotional development through psychological network analysis. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Broda, M., Wang, T. W., Joy, J. M., Hui, J., Chang, C. N., Greene, M. D., Chen, C. C., Corning, A., Xia, Y., & Liu, X. (2025, April-forthcoming). A scoping review of machine learning applications in education research. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Xie, L., Jiang, Y., Mo, F., & Chang, C. N. (2025, April-forthcoming). How college personnel embracing AI? — A mixed method study. Conference presentation at the 2025 American Educational Research Association (AERA) Annual Conference, Denver, CO.
Xia, Y., & Vanderford, C. (2025). Examining Physician Assistant Students' Attitudes Toward Artificial Intelligence as a Pedagogical Tool. Conference presentation at the 20th Annual CCTS Spring Conference, Lexington, KY.
Broda, M.D. (2024, October 3rd). Deduction or Discovery? Opportunities for Machine Learning in Enhancing Special Education Research. Invited lecture for the Vanderbilt University Department of Special Education. Nashville, TN.
Chang, C. N., Hui, J., Justus-Smith, C. D., & Wang, T. W. (2024, Apr.). AI-Supported mentoring in higher education: Optimizing myIDP with large language models. Conference presentation at the 2024 American Educational Research Association (AERA) Annual Conference, Philadelphia, PA.
Greene, M. D., & Xu, Y. (2024, March). Practical approaches to implementing AI tools in STEM education for K-12 learners. Presentation at the EdTechRVA 2024 conference. Richmond, VA.
Greene, M. D., & Xu, Y. (2024, June). Using AI tools to maximize student learning in K-12 education. Presentation at the Metropolitan Educational Research Consortium (MERC) Summit. Richmond, VA.
Greene, M.D., & Chang, C. (2024, November). Advancing educational research through machine learning and text mining (text mining across borders). Virtual presentation at the 2024 VCU Joint Research Symposium: Educator and Educator Preparation in the Global Contexts. Virginia Commonwealth University. Richmond, VA.
Greene, M.D., & Xu, Y. (2023, December). Integrating AI in K-12 STEM education. Presentation at the Virginia Society for Technology in Education (VSTE) Conference, Roanoke, VA.
Greene, M. D., & Schad, M.L. (2023, September). Navigating the AI disruption: Strategies and best practices. Presentation at the Virginia Teachers of English to Speakers of Other Languages (VATESOL) conference. Richmond, VA.
Boateng. O. A (2024, June). Assessing and Improving Student Advising Efficiency with a Probabilistic Model to Predict No-Shows in Academic Appointments. Presentation at the International Conference on Assessing Quality in Higher Education. Berlin, Germany.
Justus-Smith, C., Wang, T.W., & Youngquest, G. (2025, April 25) Unpacking Education Choice: Leveraging Machine Learning to Explore School and Homeschool Decisions. [Roundtable Session – Research In-Progress]. American Educational Research Association 2025 Annual Meeting, Denver, CO, United States.
Justus-Smith, C., Wang, T.W., & Youngquest, G. (2025, April 26) Unpacking Education Choice: Leveraging Machine Learning to Explore School and Homeschool Decisions. [Poster Session - In-Progress Research Gala for Division D: Measurement & Research Methodologies]. American Educational Research Association 2025 Annual Meeting, Denver, CO, United States.
Greene, M.D. (2025, March). Exploring the application of AI and design thinking in K-12 STEM education. Presentation at Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2025, Mar.). Navigating STEM Careers with AI Mentors. Presentation at Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2025, Feb.). Guest Talk: AI in Higher Education. Presentation at GRAD 602: Teaching and Learning in Higher Education, Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2024, Sep.). Guest Talk: Machine Learning and AI in Educational Research. Presentation at EDCI 504: AI in Education, University of Idaho. Moscow, ID.
Chang, C. N. (2024, May.). Measuring educational success in Occupational Therapy. Presentation at Department of Occupational Therapy, College of Health Professions, Virginia Commonwealth University. Richmond, VA.
Greene, M.D. et al. (2024, April). The opportunities and risks of using AI in research processes. Presentation at Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2024, April). Science Identity Pathways and STEM Career Aspirations: An Intersectional Analysis in Youth. Presentation at VCU Institute for Collaborative Research and Evaluation & VCU School of Education Office of Research and Faculty Development, Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2024, Feb.). Data-Driven Discoveries: Exploring Machine Learning Applications in Educational Research. Presentation at 2024 AI in Education Group, Virginia Commonwealth University. Richmond, VA.
Broda, M.D., Chang, C., Hui, J., Wang, T., Chen, C., & Liu, X. (2023, October 27th). What is machine learning? Key concepts and connections to education research. Invited talk for the VCU School of Education Office of Research and Professional Development. Richmond, VA.
Chang, C. N. (2023, Aug.). Artificial and Real Intelligence: Tools to Organize Your Research Skills. Presentation at Holmes Scholars Summit, Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2023, Jun.). Partitioning variance for a within-level predictor in multilevel models. Presentation at National Yang Ming Chiao Tung University, Taiwan.
Broda, M., Chang, C., & Greene, M.D. (2024, June). I want to be a data scientist: Here’s what I need to know about AI. Transformative Learning Experiences to Transform the Workforce. Workshop at Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2024, Apr.). Doctoral Colloquia: Structural Equation Modeling in R for Beginners. Workshop at 2024 Special Education and Disability Policy (SEDP), Virginia Commonwealth University. Richmond, VA.
Chang, C. N. (2024, Mar.). Every Item Every Relation All at Once: Structural Equation Modeling in Mplus. Workshop at Claremont Graduate University. Claremont, CA.