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Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

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Updated: Sep 7, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning

Daryn A Dever¹, Mary Jean Amon¹, Hana Vrzáková²

¹School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL, United States.

Frontiers in Psychology

|June 17, 2022

Summary

This summary is machine-generated.

Game-based learning environments (GBLEs) can enhance microbiology education. Students with more predictable interaction patterns and restricted agency in games showed greater learning gains, suggesting GBLEs can scaffold self-regulation.

Keywords:

auto-recurrence quantification analysis eye tracking game-based learning hierarchical modeling log files self-regulation

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Area of Science:

Educational Technology
Cognitive Science
Game-Based Learning

Background:

Game-based learning environments (GBLEs) offer interactive platforms for education.
Understanding how learners interact with instructional materials within GBLEs is crucial for optimizing learning.
The role of learner agency and interaction patterns in GBLEs requires further investigation.

Purpose of the Study:

To investigate the relationship between learner agency, interaction patterns, and learning gains in a GBLE.
To explore how different types of instructional materials and learner abilities influence engagement.
To analyze learning dynamics using non-linear dynamical systems approaches within GBLEs.

Main Methods:

82 undergraduate students learned microbiology using the Crystal Island GBLE.
Participants were assigned to full or partial agency conditions.
Log-file and eye-tracking data were analyzed using hierarchical linear growth models and auto-recurrence quantification analysis (aRQA).

Main Results:

Greater eye gaze dwell times and learning gains correlated with predictable interaction sequences.
Participants with restricted agency and more recurrent action sequences demonstrated higher learning gains.
Learner ability to distinguish relevant from irrelevant information impacted learning gains.

Conclusions:

Predictable interaction sequences and restricted agency may enhance learning in GBLEs.
GBLEs can be designed to scaffold self-regulation and optimize learning outcomes.
Non-linear dynamical systems analysis provides insights into learning processes within game-based environments.