@ARTICLE{Sychev2021mdpi,
author={Oleg Sychev and Nikita Penskoy and Anton Anikin and Mikhail Denisov and Artem Prokudin},
title={Improving comprehension: Intelligent tutoring system explaining the domain rules when students break them},
journal={Education Sciences},
year={2021},
volume={11},
number={11},
doi={10.3390/educsci11110719},
art_number={719},
url={https://www.mdpi.com/2227-7102/11/11/719},
affiliation={Software Engineering Department, Electronics and Computing Machinery Faculty, Volgograd State Technical University, Volgograd, 400005, Russian Federation},
abstract={Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower-to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
correspondence_address1={Sychev, O.; Software Engineering Department, Russian Federation; эл. почта: o_sychev@vstu.ru; Anikin, A.; Software Engineering Department, Russian Federation; эл. почта: anton.anikin@vstu.ru},
publisher={MDPI},
issn={22277102},
language={English},
abbrev_source_title={Educ. Sci.},
thanks = {rfbr-20-07-00764}
}

@InProceedings{10.1007/978-3-030-80421-3_6,
author="Sychev, Oleg
and Anikin, Anton
and Penskoy, Nikita
and Denisov, Mikhail
and Prokudin, Artem",
editor="Cristea, Alexandra I.
and Troussas, Christos",
title="CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level",
booktitle="Intelligent Tutoring Systems",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="52--59",
abstract="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.",
isbn="978-3-030-80421-3",
thanks = {rfbr-20-07-00764}
}