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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)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)Deadlines and MOOCs: How Do Students Behave in MOOCs with and without Deadlines., , , , and . FIE, page 1-9. IEEE, (2020)Learning programming, syntax errors and institution-specific factors., , , and . ACE, page 90-96. ACM, (2018)Methodological Considerations for Predicting At-risk Students., , , , and . ACE, page 105-113. ACM, (2022)Taxonomizing features and methods for identifying at-risk students in computing courses., , , , , , , , , and . ITiCSE, page 364-365. ACM, (2018)Morning or Evening? An Examination of Circadian Rhythms of CS1 Students., , , , and . ICSE (SEET), page 261-272. IEEE, (2021)Evaluating Distance Measures for Program Repair, , , , and . Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1, volume 1 of ICER '23, page 495–507. New York, NY, USA, Association for Computing Machinery, (Sep 10, 2023)