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ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
Psychology Faculty Publications
  • Arthur C. Graesser, University of Memphis
  • Xiangen Hu, University of Memphis
  • Benjamin D. Nye, University of Southern California
  • Kurt VanLehn, School of Computing, Informatics, and Decision Systems Engineering
  • Rohit Kumar, University of Memphis
  • Cristina Heffernan, Worcester Polytechnic Institute
  • Neil Heffernan, Worcester Polytechnic Institute
  • Beverly Woolf, University of Massachusetts Amherst
  • Andrew M. Olney, University of Memphis
  • Vasile Rus, University of Memphis
  • Frank Andrasik, University of Memphis
  • Philip Pavlik, University of Memphis
  • Zhiqiang Cai, University of Memphis
  • Jon Wetzel, School of Computing, Informatics, and Decision Systems Engineering
  • Brent Morgan, University of Memphis
  • Andrew J. Hampton, University of Memphis
  • Anne M. Lippert, University of Memphis
  • Lijia Wang, University of Memphis
  • Qinyu Cheng, University of Memphis
  • Joseph E. Vinson, University of Memphis
  • Craig N. Kelly, University of Memphis
  • Cadarrius McGlown, University of Memphis
  • Charvi A. Majmudar, University of Memphis
  • Bashir Morshed, University of Memphis
  • Whitney Baer, University of Memphis
Document Type
Article
Publication Date
12-1-2018
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.

Citation Information
Arthur C. Graesser, Xiangen Hu, Benjamin D. Nye, Kurt VanLehn, et al.. "ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics" (2018)
Available at: http://works.bepress.com/anne-lippert/12/