ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics

Autor/inn/en	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
Titel	ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics
Quelle	In: International Journal of STEM Education, 5 (2018), Artikel 15 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2196-7822
DOI	10.1186/s40594-018-0110-y
Schlagwörter	Intelligent Tutoring Systems; STEM Education; Electronics; Integrated Curriculum; Partnerships in Education; Military Training; Client Server Architecture; Tennessee (Memphis); Arizona; Massachusetts + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; STEM; Elektronik; Hochschulpartnerschaft; Militärausbildung; Master-Studiengang
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01