Artificial Intelligence (AI) and Data Science

• How AI is transforming education: Purdue Expert https://youtu.be/dDtE1bvB-VM
• AI in Education: Bill Watson – “Education in the (dis)Information Age” https://youtu.be/XHG_loMH1-A
• DiCerbo, Kristen; Wright, Wayne, et. al. (2024, April 19). AI-ED Fusion: Symposium on STEM Education in the Age of AI. Purdue University. https://youtu.be/2Cg8AJeVnsc
• Grant, Melva; Kastberg, Signe; et al. (2024, April 5). Power Friday: Emerging Educational Technologies – Enhancing Learning with VR, XR, & AI [Presentation]. Power Friday, Purdue University. https://youtu.be/pnI9DuhsRSA
• Cheng, Y. (2024, March 28). How Early, Accurate, and Fair Can We Predict Student Learning in Foundational STEM Courses? [Presentation]. Craft Data Science Insights: A 40-Minute Exploration, Purdue University. https://youtu.be/T1xH3S_PAFo
• Zhang, S. (2024, March 26). Exploring the NAEP Math Achievement Gap: Insights from Test-Taking Process Data [Presentation]. Craft Data Science Insights: A 40-Minute Exploration, Purdue University. https://youtu.be/_zSWlyBsuQ8
• Zheng, C. (2024, March 20). Psychometrics Empowering Large Language Models in Chinese Essay Automated Scoring [Presentation]. Craft Data Science Insights: A 40-Minute Exploration, Purdue University. https://youtu.be/2AmBLN0C5m8?si=R-RS7KCa59HrY1O-
• He, Q. (2024, February 26). Sequence Mining on Process Data in Digital-Based Large-Scale Assessments [Presentation]. Craft Data Science Insights: A 40-Minute Exploration, Purdue University. https://www.youtube.com/watch?v=9Dl1ewGxTRA
• Kwon, K. (2023, September 15). AI Education: Lessons Learned for Empowering Teachers and Enriching Student Learning [Presentation]. Power Friday, Purdue University. https://youtu.be/5MUmoVgbbB0

Huang, W., & Yu, W. (2024, April 14). Top-down vs. Grassroot: A comparative study on AI integration barriers in K-12 in South Korea and the United States [Conference presentation]. 2024 American Educational Research Association (AERA) Annual Meeting, Philadelphia, PA, United States. https://www.aera.net/

Yu, W., & Huang, W. (2024, April 11). AI is no more a magic box: A case study of an AI educational program [Conference presentation]. 2024 American Educational Research Association (AERA) Annual Meeting, Philadelphia, PA, United States. https://www.aera.net/

Sirnoorkar, A. (2024, February). Leveraging Generative-AI in physics education. Physics Education Research group seminar, Department of Physics and Astronomy, Purdue University.

Jiang, Y., Xie, L., Lin, G., & Mo, F. Widening the Debate: The Academic Community’s Perception of ChatGPT. 2024 American Educational Research Association (AERA) Conference. Philadelphia, PA.

Yu, W., Kang, S., & Huang, W. (2023, December 1). C.A.R.E (computer science, artificial intelligence, robotics, and emerging technologies) @ Purdue [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/trustworthy-ai-lab-for-education-summit/

Jiang, Y. (2023, December 1). Exploring ChatGPT: Policy and Regulation of Digital Frameworks Through Twitter Discourse [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/trustworthy-ai-lab-for-education-summit/

Huang, W., & Yu, W. (2023, December 1). Artificial intelligence (AI) and education – voices from K-12 educators in South Korea and the United States [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/trustworthy-ai-lab-for-education-summit/

Chang, H. (2023, December 1). Integrating Psychometric Tools into AI Development [Panel Presentation]. Trustworthy AI Lab for Education Summit. https://www.youtube.com/watch?v=Lj3ZX1GIW1o&list=PL-8_PytklE-NbIgVRNqPXdeMlfsbV9PDE&index=2

Sirnoorkar, A., Laverty, J.T., Zollman, D., Rebello, S. & Bryan, L.A. (2023, December). Exploring student and AI generated assumptions to varying degree of promoting. Proceedings, International Conference on Physics Education (ICPE), India.

Huang, W. & Yu, W. (2023, November 10). The collaboration between AI and educators. Indiana Association of Colleges for Teacher Education (IACTE) webinar. https://www.inaacte.org/

Huang, W., & Yu, W. (2023, October 15-19). AI education integration in South Korea from early adopter’s perspectives: A case study [Conference presentation]. AECT 2023 Convention, Orlando, FL, United States. https://convention.aect.org/

Chang, H. (2023, October 4). Building Adaptive Testing Tools to Improve Classroom Assessment in Large Introductory STEM [Presentation]. Center for Astrophysics, Harvard & Smithsonian Science Education, CfA Science Education Department, Harvard University. https://www.youtube.com/watch?v=gCmfTv3fNuE

Sirnoorkar, A. (2023, October). Development and analysis of assessments that promote sensemaking in physics. Physics Education Research group seminar, Department of Physics and Astronomy, Purdue University.

Yu, W. (2023, April 13-16). Investigating the barriers to introducing Artificial Intelligence (AI) education for elementary school teachers in South Korea [Conference presentation]. 2023 AERA Annual Meeting, Chicago, IL, United States. https://www.aera.net/Events-Meetings

Chang, H. (2022, June 15). A New Perspective of Computerized Adaptive Testing (CAT): From Testing to Personalized Learning [Presentation]. 2022 NCME Career Contribution Award Presentation. https://youtu.be/X15kcYkGUgA?si=0i7HT5GskpMfjMIM

Chang, H. (2018, April 14). From Adaptive Testing to Adaptive Learning [AERA E.F. Lindquist Award Lecture]. 2018 American Educational Research Association (AERA) Annual Meeting. New York, NY. https://youtu.be/1jZv3b5_-QE?si=58ztPGYTbBKWqXKi

Jiang, Y., Xie, L., Lin, G., & Mo, F. (2024). Widen the debate: What is the academic community’s perception on ChatGPT?. Education and Information Technologies, 1-20. https://doi.org/10.1007/s10639-024-12677-0

Xin, Y. P., Tzur, Si, L. Hord, C., Liu, J., Park, J. Y. (2017). An intelligent tutor-assisted math problem-solving intervention program for students with learning difficulties. Learning Disability Quarterly, 40(1), 4-16. doi:10.1177/0731948716648740

Cetintas, S., Si, L., Xin, Y. P., & Tzur, R. (2013, October). Probabilistic latent class models for predicting student performance. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 1513-1516). ACM

Cetintas, S, Si, L., Xin, Y. P., Zhang, D., Park, J. Y. & Tzur, R. (2010). A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems. Journal of Educational Data Mining, 2(1), 83-101.

Cetintas, S., Si, L., Xin, Y. P., and Hord, C. (2010). Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques. IEEE Transactions on Learning Technologies, 3( 3), 228-236.

Cetintas, S., Si, L., Xin, Y. P., Hord, C. (2010). Predicting Correctness of Problem Solving in ITS with a Temporal Collaborative Filtering Approach. In Vincent Aleven, Judy Kay, Jack Mostow, editors, Intelligent Tutoring Systems, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, Proceedings, Part I. Volume 6094 of Lecture Notes in Computer Science, pages 15-24, Springer.

Cetintas, S., Si, L., Xin, Y. P. and Hord, C. (2009). Predicting correctness of problem solving from low-level log data in intelligent tutoring systems. In Proceedings of the 2nd International Conference on Educational Data Mining (EDM)

Cetintas, S., Si, L., Xin, Y. P., Hord, C. & Zhang, D. (2009). Learning to identify students’ off-task behavior in intelligent tutoring systems. In V. Dimitrova, R. Mizoguchi, B. du Boulay, A. Graesser (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 701-¬703). Amsterdam, Netherlands: IOS Press.

Cetintas, S., Si, Luo., Xin, Y. P., Zhang, D., Park, J. (2009). Automatic text classification of mathematical word problems. In Proceedings of the 22nd International FLAIRS Conference (pp. 27-32). AAAI Press.

Ozen, Z. (2024). Developing a first-level gifted identification tool: A machine learning application. Dissertation Chair: Nielsen Pereira

Karakis, N. (2021). Predictors of early postsecondary stem persistence of high-achieving students: an explanatory study using machine learning techniques. Dissertation Chair: Nielsen Pereira