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