Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom’s taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners’ thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.
CITATION STYLE
Sychev, O., Anikin, A., Penskoy, N., Denisov, M., & Prokudin, A. (2021). CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12677 LNCS, pp. 52–59). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80421-3_6
Mendeley helps you to discover research relevant for your work.