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Automated Disengagement Tracking Within an Intelligent Tutoring System.

Su Chen^1,2, Ying Fang^2,3, Genghu Shi^2,3

¹Department of Mathematical Sciences, University of Memphis, Memphis, TN, United States.

Frontiers in Artificial Intelligence

|March 22, 2021

Summary

This summary is machine-generated.

A new automated disengagement tracking system (DTS) accurately identifies when adult literacy learners in an intelligent tutoring system (ITS) are disengaged. Disengaged learners showed significantly lower accuracy, impacting reading comprehension assessment.

Keywords:

AutoTutor conversational agents disengagement intelligent tutoring system mind wandering

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Area of Science:

Educational Technology
Artificial Intelligence in Education
Human-Computer Interaction

Background:

Intelligent tutoring systems (ITS) aim to enhance learning outcomes.
Detecting learner disengagement is crucial for effective adaptive learning.
Previous methods for disengagement detection often rely on self-reports, which can be unreliable.

Purpose of the Study:

To introduce and validate an automated disengagement tracking system (DTS) for intelligent tutoring systems.
To assess the effectiveness of DTS in identifying maladaptive behaviors like mind-wandering and impetuous responding.
To examine the relationship between detected engagement levels and reading comprehension performance.

Main Methods:

Developed an unsupervised learning-based DTS to detect disengagement in AutoTutor, a conversational ITS for adult literacy.
Established a baseline performance using initial response accuracy and time.
Identified disengagement by detecting significant deviations from the established baseline.

Main Results:

The DTS identified disengaged items with significantly lower accuracy (18.5%) compared to engaged items (71.8%).
Learner response time and accuracy were analyzed for 252 adult literacy learners.
Reading comprehension scores were significantly associated with engaged item accuracy, but not disengaged item accuracy.

Conclusions:

The automated disengagement tracking system effectively distinguishes between engaged and disengaged learning states.
Disengagement significantly impacts performance accuracy within the intelligent tutoring system.
Accurate engagement detection is vital for reliable assessment of reading comprehension skills in ITS.