Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

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...

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Autores principales:	Oleg Sychev, Nikita Penskoy, Anton Anikin, Mikhail Denisov, Artem Prokudin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Bloom’s taxonomy adaptive learning cognitive learning constraint-based tutors intelligent tutoring system Education L
Acceso en línea:	https://doaj.org/article/5b109f68bfe34ce99f6f8813b13fb916
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id	oai:doaj.org-article:5b109f68bfe34ce99f6f8813b13fb916
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spelling	oai:doaj.org-article:5b109f68bfe34ce99f6f8813b13fb9162021-11-25T17:23:39ZImproving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them10.3390/educsci111107192227-7102https://doaj.org/article/5b109f68bfe34ce99f6f8813b13fb9162021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7102/11/11/719https://doaj.org/toc/2227-7102Intelligent 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.Oleg SychevNikita PenskoyAnton AnikinMikhail DenisovArtem ProkudinMDPI AGarticleBloom’s taxonomyadaptive learningcognitive learningconstraint-based tutorsintelligent tutoring systemEducationLENEducation Sciences, Vol 11, Iss 719, p 719 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bloom’s taxonomy adaptive learning cognitive learning constraint-based tutors intelligent tutoring system Education L
spellingShingle	Bloom’s taxonomy adaptive learning cognitive learning constraint-based tutors intelligent tutoring system Education L Oleg Sychev Nikita Penskoy Anton Anikin Mikhail Denisov Artem Prokudin Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
description	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.
format	article
author	Oleg Sychev Nikita Penskoy Anton Anikin Mikhail Denisov Artem Prokudin
author_facet	Oleg Sychev Nikita Penskoy Anton Anikin Mikhail Denisov Artem Prokudin
author_sort	Oleg Sychev
title	Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
title_short	Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
title_full	Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
title_fullStr	Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
title_full_unstemmed	Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them
title_sort	improving comprehension: intelligent tutoring system explaining the domain rules when students break them
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5b109f68bfe34ce99f6f8813b13fb916
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Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

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