The Impact of Granularity on Worked Examples and Problem Solving.
In this paper, we explored the impact of two types of instructional interventions, worked examples and problem solving, at two levels of granularity: problems and steps. This study relied on an existing Intelligent Tutoring System (ITS) for Probability called Pyrenees and involved 266 students who were randomly assigned into five conditions. All students experienced the same procedure, studied the same training problems in the same order, and used the same ITS. The conditions differed only in how the training problems were presented. Our results showed that when the domain content and required steps are strictly equivalent, different granularities of pedagogical decisions can significantly impact studentsﾒ time on task. More specifically, the fine-grained step level decisions can have a stronger pedagogical impact than the problem-level ones.
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B. Mostafavi, G. Zhou, C. F. Lynch, M. Chi, and T. Barnes. ﾓData-driven Worked Examples Improve Retention and Completion in a Logic Tutorﾔ. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015). 2015, pp. 726 ﾖ729.
Mostafavi, B., Liu, Z. and Barnes, T. (2015) Data-driven Proficiency Profiling. In Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), In Press.
Mostafavi, B., Eagle, M. and Barnes T (2015, March). Towards Data-driven Mastery Learning. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK 2015), pp. 270-274. ACM.
C. F. Lynch, T. W. Price, M. Chi, and T. Barnes. ﾓUsing the Hint Factory to Analyze Model-Based Tutoring Systemsﾔ. In: Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). (in press). CEUR-WS, 2015.
T. W. Price, C. F. Lynch, T. Barnes, and M. Chi. ﾓAn Improved Data-Driven Hint Selection Algorithm for Probability Tutorsﾔ. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015). International Educational Data Mining Society, 2015. pp 610 - 611
Michael Eagle, Drew Hicks, and Tiffany Barnes. ﾓInteraction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments.ﾔ In Proceedings of the 8th International Conference on Educational Data Mining (EDM 15), Madrid, Spain, 2015
Michael Eagle, Drew Hicks, Barry Peddycord III, and Tiffany Barnes. ﾓExploring Networks of Problem-Solving Interactions.ﾔ In Proceedings of the 5th International Learning Analytics and Knowledge Conference (LAK 15), Poughkeepsie, New York, 2015
Zhou, G., Price, T. W., Lynch, C., Barnes, T., & Chi, M. (2015). The Impact of Granularity on Worked Examples and Problem Solving. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society. (PP. 2817-2822) [Oral Presentation]