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

Abstract

BackgroundThe 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.ResultsA 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.ConclusionsThe 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.

A survey of adaptive context-aware learning environments

“…The adaptation taxonomy is linked to the pedagogical taxonomy (Table A4) through target of adaptation as in a majority of the reviewed ACALEs the adaptation target is related to teaching or learning: content se-lection, learning content sequence, learning activity assessment or feedback type [46,89,125]. The pedagogical taxonomy is linked to the context-awareness taxonomy as well by means of pedagogical context entities which play the role of a utilized contextual entity in some of the surveyed ACALEs [1,101,123].…”

Advances from the Office of Naval Research STEM Grand Challenge: expanding the boundaries of intelligent tutoring systems

“…The first is the development of ElectronixTutor to help sailors learn about electronic circuits. University of Memphis (see Graesser et al 2018, current issue) led this effort in an ITS that integrated components from all four of the teams in phase 1 of the ONR STEM Challenge. This was a unique effort that attempts to deliver the right learning resource for the right sailor at the right time, based on the sailor's prior performance history.…”

“…The fourth article by Graesser et al (2018), ElectronixTutor: An intelligent tutoring system with multiple learning resources for electronics, was a monumental contribution by 25 authors from academia and industry across eight different institutions. The ElectronixTutor plays off the success and lessons learned from the SKOPE-IT project to create an intelligent learning resource that integrates elements from five highly successful intelligent Tutoring Systems (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as traditional reading of text materials.…”

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

“…Abhijeet Kumar et al in [10], proposes the adaptive question bank development (AQBDMS), based on certain criteria like Bloom's taxonomy, difficulty level, marking scheme etc., and a management system that aims to generate balanced combination of questions intelligently as per parameter provided by the question paper designer (QPD) and become a great aid to the organisation in effective question modelling and its assessment. Arthur C Graesser et al in [11] proposes ElectronixTutor, an intelligent tutoring system with multiple learning resources for electronics. The latest research we can see is the work of Moiz Uddin Ahmed et al in [3], that develop A Model of Adaptive E-Learning in an Online Distance Learning Environment which consists three sub models such as content model, learner model and adaptive model, that can improve the knowledge level of the students.…”