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

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.