Our Projects

Current and Completed Projects

Sample Research Projects

• Whitmer, J., Pedro, S. S., Liu, R., Walton, K. E., Moore, J. L., & Lotero, A. A. (2019). The constructs behind the clicks. ACT Research Report, 26.

• Whitmer, J., Nasiatka, D., & Harfield, T. (2018). Do students notice notifications? Large-scale analysis of student interactions with automated messages about grade performance. Proceedings of the 8th International Conference on Learning Analytics and Knowledge.

• Whitmer, J. (n.d.) The impact of student-facing LMS dashboards. Blackboard.

• Dietrichson, A., Forteza, D., & Whitmer, J. (2019). Meta-predictive retention risk modeling: risk model readiness assessment at scale with x-ray learning analytics. In LALA (pp. 79-88).

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• Andres, J. M. A. L., Hutt, S., Ocumpaugh, J., Baker, R.S., Nasiar, N., & Porter, C. (2021). How anxiety affects affect: A quantitative ethnographic investigation using affect detectors and data targeted interviews. In Proceedings of the 3rd International  Conference on Quantitative Ethnography.

• Andres, J. M., Baker, R. S., Hutt, S. J., Mills, C., Zhang, J., Rhodes, S., & DePiro, A. (2023). Anxiety, achievement, and self-regulated learning in CueThink. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 258-265.

• Ocumpaugh, J., Hutt, S., Andres, J. M. A. L., Baker, R. S., Biswas, G., Bosch, N., Paquette, L. & Munshi, A. (2021). Using Qualitative Data from Targeted Interviews to Inform Rapid AIED Development. In Proceedings of the 29th International Conference on Computers in Education. 

• Andres, J. M. A. L. (2023). Finding the Panic Button: Contextualizing Anxiety Within Interactive Learning Environments (Doctoral dissertation, University of Pennsylvania).

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• Li, W. & Brooks, C. (2024, July). Influence on Judgements of Learning Given Perceived AI Annotations. In Proceedings of the 10th ACM Conference on Learning@Scale (pp. TBD).

• Singh, A., Brooks, C., Wang, X., Li, W., Kim, J., & Wilson, D. (2024, March). Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation. In Proceedings of the 14th International Learning Analytics and Knowledge Conference (pp. 742-748).

• Li, W., Sun, K., Schaub, F., & Brooks, C. (2022). Disparities in Students’ Propensity to Consent to Learning Analytics. International Journal of Artificial Intelligence in Education, 32(3), 564-608.

• Li, W., Brooks, C., & Schaub, F. (2019, March). The Impact of Student Opt-Out on Educational Predictive Models. In Proceedings of the 9th International Learning Analytics and Knowledge Conference (pp. 411-420).

Technical Assistance

Learning Engineering Virtual Institute - Trialing Hub

In our capacity as the Trialing Hub for LEVI, we have completed review and enhancement of project planning and reporting documents to ensure clarity, precision, and alignment with project goals. We have created dataset templates and descriptions for metadata, ensuring that data is accessible, understandable, and usable. Our approach involves integrating feedback and modifying synthesis documents to reflect new insights and findings, ensuring common understanding and responsive guidelines. We have synthesized research studies, compiled metadata, and generated comprehensive summary reports, offering a holistic view of completed research studies. We have also developed categorization frameworks to classify research studies based on research design methods and outcome variables, providing invaluable insights into project impacts. Additionally, we coordinate with other research hubs to support overarching goals like LEVI's mission to double math learning among critical populations, fostering collaboration and driving success. 

 AIMS Collaboratory

 Inspira/Assessment Provider

Current Partners