Maciej Pankiewicz

Senior Research Investigator, Penn Center for Learning Analytics

Education Culture and Society Division

Visiting Assistant Professor

Contact

mpank@upenn.edu

Education

Ph.D. (Dr.-Ing.) University of Bonn, 2011
M.Sc. (Computer Science and Econometrics) Warsaw University of Life Sciences, 2006

Areas of Expertise

Educational data mining
Learning analytics
Learning engineering

Programs

Learning Analytics M.S.Ed. (Online)

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Biography

Professional Biography

Maciej Pankiewicz has extensive experience as a software developer, data analyst, and researcher in educational data mining. His technical expertise encompasses a wide range of IT skills such as software development, data management, and predictive modeling using various technologies and programming languages, including Python, SQL, R, and RapidMiner. This expertise has been instrumental in his latest research work, aimed at designing intelligent educational systems using large language models.

Research Interests and Current Projects

Maciej Pankiewicz is engaged in several projects at the Penn Center for Learning Analytics. His most recent project is JeepyTA, a virtual teaching assistant. JeepyTA is powered by large language models to improve student and instructor experiences. It is being developed at the Penn Center for Learning Analytics at the University of Pennsylvania.

Publications

Selected Publications

Pankiewicz, M., Baker, R., & Ocumpaugh, J. (2023). Using intelligent tutoring on the first steps of learning to program: Affective and learning outcomes. In W. N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium and Blue Sky 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (T. 1831, s. 593–598). https://doi.org/10.1007/978-3-031-36336-8_92

Pankiewicz, M., & Baker, R. S. (2023). Large language models (GPT) for automating feedback on programming assignments. In W. J.-L. Shin, A. Kashihara, W. Chen, & H. Ogata (Eds.), 31st International Conference on Computers in Education Conference Proceedings, Volume I (s. 68–77). Asia-Pacific Society for Computers in Education (APSCE).

Cloude, E., Baker, R. S., & Pankiewicz, M. (2023). Measuring self-regulated learning processes in computer science education. In W. J.-L. Shin, A. Kashihara, W. Chen, & H. Ogata (Eds.), 31st International Conference on Computers in Education Conference Proceedings, Volume I (s. 406–408). Asia-Pacific Society for Computers in Education (APSCE).

Pankiewicz, M. (2023). Evaluating the predictive performance of quick methods for estimating task difficulty and student ability in automated computer programming assessment. In W. T. J. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning 2023 (s. 1440–1445). Association for the Advancement of Computing in Education (AACE).

Pankiewicz, M., & Bator, M. (2021). On-the-fly estimation of task difficulty for item-based adaptive online learning environments. In W. C. Schulte & B. A. Becker (Eds.), ITiCSE ’21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1, 2 (s. 317–323). https://doi.org/10.1145/3430665.3456305