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ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics

BACKGROUND: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed syste...

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Autores principales: Graesser, Arthur C., Hu, Xiangen, Nye, Benjamin D., VanLehn, Kurt, Kumar, Rohit, Heffernan, Cristina, Heffernan, Neil, Woolf, Beverly, Olney, Andrew M., Rus, Vasile, Andrasik, Frank, Pavlik, Philip, Cai, Zhiqiang, Wetzel, Jon, Morgan, Brent, Hampton, Andrew J., Lippert, Anne M., Wang, Lijia, Cheng, Qinyu, Vinson, Joseph E., Kelly, Craig N., McGlown, Cadarrius, Majmudar, Charvi A., Morshed, Bashir, Baer, Whitney
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310412/
https://www.ncbi.nlm.nih.gov/pubmed/30631705
http://dx.doi.org/10.1186/s40594-018-0110-y
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author Graesser, Arthur C.
Hu, Xiangen
Nye, Benjamin D.
VanLehn, Kurt
Kumar, Rohit
Heffernan, Cristina
Heffernan, Neil
Woolf, Beverly
Olney, Andrew M.
Rus, Vasile
Andrasik, Frank
Pavlik, Philip
Cai, Zhiqiang
Wetzel, Jon
Morgan, Brent
Hampton, Andrew J.
Lippert, Anne M.
Wang, Lijia
Cheng, Qinyu
Vinson, Joseph E.
Kelly, Craig N.
McGlown, Cadarrius
Majmudar, Charvi A.
Morshed, Bashir
Baer, Whitney
author_facet Graesser, Arthur C.
Hu, Xiangen
Nye, Benjamin D.
VanLehn, Kurt
Kumar, Rohit
Heffernan, Cristina
Heffernan, Neil
Woolf, Beverly
Olney, Andrew M.
Rus, Vasile
Andrasik, Frank
Pavlik, Philip
Cai, Zhiqiang
Wetzel, Jon
Morgan, Brent
Hampton, Andrew J.
Lippert, Anne M.
Wang, Lijia
Cheng, Qinyu
Vinson, Joseph E.
Kelly, Craig N.
McGlown, Cadarrius
Majmudar, Charvi A.
Morshed, Bashir
Baer, Whitney
author_sort Graesser, Arthur C.
collection PubMed
description BACKGROUND: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. RESULTS: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. CONCLUSIONS: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.
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spelling pubmed-63104122019-01-08 ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics Graesser, Arthur C. Hu, Xiangen Nye, Benjamin D. VanLehn, Kurt Kumar, Rohit Heffernan, Cristina Heffernan, Neil Woolf, Beverly Olney, Andrew M. Rus, Vasile Andrasik, Frank Pavlik, Philip Cai, Zhiqiang Wetzel, Jon Morgan, Brent Hampton, Andrew J. Lippert, Anne M. Wang, Lijia Cheng, Qinyu Vinson, Joseph E. Kelly, Craig N. McGlown, Cadarrius Majmudar, Charvi A. Morshed, Bashir Baer, Whitney Int J STEM Educ Research BACKGROUND: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. RESULTS: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. CONCLUSIONS: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment. Springer International Publishing 2018-04-16 2018 /pmc/articles/PMC6310412/ /pubmed/30631705 http://dx.doi.org/10.1186/s40594-018-0110-y Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Graesser, Arthur C.
Hu, Xiangen
Nye, Benjamin D.
VanLehn, Kurt
Kumar, Rohit
Heffernan, Cristina
Heffernan, Neil
Woolf, Beverly
Olney, Andrew M.
Rus, Vasile
Andrasik, Frank
Pavlik, Philip
Cai, Zhiqiang
Wetzel, Jon
Morgan, Brent
Hampton, Andrew J.
Lippert, Anne M.
Wang, Lijia
Cheng, Qinyu
Vinson, Joseph E.
Kelly, Craig N.
McGlown, Cadarrius
Majmudar, Charvi A.
Morshed, Bashir
Baer, Whitney
ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title_full ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title_fullStr ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title_full_unstemmed ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title_short ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
title_sort electronixtutor: an intelligent tutoring system with multiple learning resources for electronics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310412/
https://www.ncbi.nlm.nih.gov/pubmed/30631705
http://dx.doi.org/10.1186/s40594-018-0110-y
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