Design and Implementation of a Personalized Tutoring System in an Ill-Defined Context
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Buckley, Stephen J.
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Abstract
Personalized learning refers to altering instruction to fit individuals’ needs and abilities. It is an area of educational research that has been of significant interest as the access to and utilization of technology has increased over the past decade. Personalized learning has been successfully implemented numerous times in well-defined contexts, where intelligent tutors have knowledge of the specific problems and solutions learners are working on. However, there is little research on personalized learning in ill-defined contexts where the goals of learners are not easily quantifiable. In this research, we present the design and implementation of an intelligent tutoring system in the Virtual Ecological Research Assistant (VERA) application, which is an ecological modeling application that represents an ill-defined context. The tutoring system consists of two tutors, the recommendation tutor and the exploration tutor, which are catered to two different types of learners using VERA. The tutors give feedback to learners using metacognitive scaffolding and intelligent analysis on the learners’ modeling behaviors and outputs. We also show preliminary analysis on the tutoring systems’ internal AI algorithms, and we found that the system shows the potential to serve as a framework for conducting intelligent tutoring in ill-defined contexts.
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Undergraduate Research Option Thesis