Work

Assessing Student Self-Explanations in an Intelligent Tutoring System

Year: 2007

Type: article

Abstract: Research indicates that guided feedback facilitates learning, whether in the classroom or with Intelligent Tutoring Systems (ITS).Improving the accuracy of the evaluation of user input is therefore necessary for providing optimal feedback.This study investigated an automated assessment of students' input that involved a lexico-syntactic (entailment) approach to textual analysis along with a variety of other textual assessment measures.The corpus consisted of 357 student responses taken from a recent experiment with iSTART, an ITS that provides students with self-explanation and reading strategy training.The results of our study indicated that the entailment approach provided the highest single measure of accuracy for assessing input when compared to the other measures in the study.A set of indices working in conjunction with the entailment approach provided the best overall assessments.

Source: eScholarship (California Digital Library)

Authors: Vasile Rus, Philip M. McCarthy, Arthur C. Graesser, Mihai Lintean, Danielle S. McNamara

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funder: National Science Foundation

Award: Promoting Active Reading Strategies to Improve Students' Understanding of Science

OpenAlex ID: W2585630816

FWCI: 1.071

Cites: 16

Cited by: 7

Related to: 10