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

Arthur C. Graesser, Xiangen Hu, Benjamin D. Nye, Kurt VanLehn, Rohit Kumar, Cristina Heffernan, Neil Heffernan, Beverly Woolf, Andrew M. Olney, Vasile Rus, Frank Andrasik, Philip Pavlik, Zhiqiang Cai, Jon Wetzel, Brent Morgan, Andrew J. Hampton, Anne M. Lippert, Lijia Wang, Qinyu Cheng, Joseph E. VinsonCraig N. Kelly, Cadarrius McGlown, Charvi A. Majmudar, Bashir Morshed, Whitney Baer

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish (US)
Article number15
JournalInternational Journal of STEM Education
Volume5
Issue number1
DOIs
StatePublished - Dec 1 2018

Keywords

  • ASSISTments
  • AutoTutor
  • Dragoon
  • Electronics
  • Intelligent tutoring systems
  • System integration

ASJC Scopus subject areas

  • Education

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    Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K., Kumar, R., Heffernan, C., Heffernan, N., Woolf, B., Olney, A. M., Rus, V., Andrasik, F., Pavlik, P., Cai, Z., Wetzel, J., Morgan, B., Hampton, A. J., Lippert, A. M., Wang, L., Cheng, Q., ... Baer, W. (2018). ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of STEM Education, 5(1), [15]. https://doi.org/10.1186/s40594-018-0110-y