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Flow with an intelligent tutor: A latent variable modeling approach to tracking flow during artificial tutoring
Hyeon-Ah Kang1, Adam Sales2, Tiffany A Whittaker3
1University of Texas at Austin, Austin, TX, USA. hkang@austin.utexas.edu.
Researchers explored latent variable models to track student learning flow in intelligent tutoring systems. These methods reveal learning behaviors and interaction patterns, offering insights for educational technology development.
Area of Science:
- Educational Technology
- Learning Analytics
- Psychometrics
Background:
- Intelligent tutoring systems (ITS) are increasingly used in education.
- Effective analysis of student learning behaviors within ITS is crucial.
- Latent variable modeling offers a promising approach for understanding complex learning processes.
Purpose of the Study:
- To explore latent variable modeling for tracking learning flow in computer-interactive tutoring.
- To compare three discrete profile models: latent class, latent transition, and hidden Markov models.
- To demonstrate the application of these models using real log data.
Main Methods:
- Application of latent class model (LCM).
- Application of latent transition model (LTM).
- Application of hidden Markov model (HMM).
- Analysis of log data from Cognitive Tutor Algebra I.
Main Results:
- The models successfully revealed substantive information about student learning behaviors.
- Consistent findings on learning flow states and interaction modalities were observed across models.
- Despite differing assumptions and data constraints, models provided convergent insights.
Conclusions:
- Latent variable models are valuable for describing learning flow in intelligent tutoring systems.
- The study highlights the utility of LCM, LTM, and HMM for educational data mining.
- Further development is needed to enhance the capabilities and applications of these models.
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