A prototype of this intelligent tutoring system has been developed and is currently being tested. The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. The individual components of ElectronixTutor have shown learning gains in previous decades of research. 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 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. ResultsĪ fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos.
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. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. 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. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. 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.