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Investigating student interactions with tutorial dialogues in EER-Tutor.

Myse Elmadani1, Antonija Mitrovic1, Amali Weerasinghe2

  • 11Intelligent Computer Tutoring Group, University of Canterbury, Christchurch, New Zealand.

Research and Practice in Technology Enhanced Learning
|January 8, 2019
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Summary

Analyzing student interactions with intelligent tutoring systems (ITS) using eye-gaze and interaction data reveals how novices and advanced learners differ. This insight helps create more adaptive learning environments by predicting student errors.

Keywords:
Constraint-based intelligent tutoring systemData miningEye trackingTutorial dialogues

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Intelligent tutoring systems (ITS) can adapt to learners using various data sources.
  • Eye-movement tracking and interaction logs offer complementary insights into student behavior.
  • Proactive adaptation in ITS requires understanding student engagement and difficulties.

Purpose of the Study:

  • To investigate student interaction with tutorial dialogues in the Enhanced Entity-Relationship (EER)-Tutor ITS.
  • To analyze how eye-gaze data and system logs reveal differences between novice and advanced learners.
  • To identify predictors of future student errors within the ITS context.

Main Methods:

  • Collected and analyzed eye-gaze data and student-system interaction logs from students using EER-Tutor.
  • Compared interaction patterns of novice and advanced learners during tutorial dialogues.
  • Developed predictive models for student errors based on prior knowledge, problem complexity, and performance metrics.

Main Results:

  • Advanced students exhibit focused visual attention on relevant interface areas, unlike novices who attend to irrelevant components.
  • Novice learners are often unaware of their need for assistance within tutorial dialogues.
  • Student's prior knowledge, problem complexity, and correct response rate predict future errors.

Conclusions:

  • Eye-gaze and interaction data provide valuable information for real-time adaptation of ITS.
  • Findings enable the classification of students into novice and advanced groups for tailored feedback and interventions.
  • Enhanced EER-Tutor can be improved to better support learning through adaptive strategies.