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Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials.

, , , and . ICSE-SEET, page 4-12. IEEE Computer Society, (2017)

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Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses, , , and . Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, page 76--84. New York, NY, USA, ACM, (2017)Subgoal Labeled Worked Examples in K-3 Education., and . SIGCSE, page 616-621. ACM, (2018)Computing Education in the Era of Generative AI., , , , , , , , , and . CoRR, (2023)Automatically Generating CS Learning Materials with Large Language Models., , , , , , , and . CoRR, (2022)Computing Education Research in Finland., , , , , , , , , and 3 other author(s). Past, Present and Future of Computing Education Research, Springer, (2023)Synthesizing research on programmers' mental models of programs, tasks and concepts - A systematic literature review., , , and . Inf. Softw. Technol., (December 2023)Time-on-Task Metrics for Predicting Performance., , and . SIGCSE (1), page 871-877. ACM, (2022)Evaluating Distance Measures for Program Repair., , , , and . ICER (1), page 495-507. ACM, (2023)Taxonomizing features and methods for identifying at-risk students in computing courses., , , , , , , , , and . ITiCSE, page 364-365. ACM, (2018)Methodological Considerations for Predicting At-risk Students., , , , and . ACE, page 105-113. ACM, (2022)