ORCID

0009-0005-8240-1733

Keywords

Self-regulated Learning (SRL), Gaming-the-system, Game-based Learning Environments (GBLEs), Sequence Analysis, Trace data and Log-files

Abstract

Consistently monitoring and regulating cognitive and metacognitive skills during learning is difficult. As a result, game-based learning environments (GBLEs) have been designed to provide richer learning experiences. They foster self-regulated learning (SRL) skills and offer a way to study learners' interactions within complex environments. Although SRL in GBLEs has been widely studied, large gaps remain. For example, it is unclear how improper or maladaptive regulation in GBLEs signals gaming-the-system, in which learners manipulate features to avoid authentic learning. This phenomenon is widely studied in intelligent tutoring systems (ITSs). Limited research has examined how and when both gaming-the-system and SRL behaviors unfold within a learning session, and how these patterns affect learning and performance. Previous research often examined overall frequencies of SRL behaviors. In contrast, this thesis used high-school learners' (N = 204) log files collected while they used Crystal Island to learn about microbiology, which were subsequently analyzed through a dynamic lens of SRL, accounting for the temporally unfolding processes during learning. Using dynamic time warping and sequence analysis, I identify distinct clusters of learners with similar in-game behavioral patterns. These clusters show how gaming-the-system and SRL behaviors affect task performance and learning outcomes in the narrative-based GBLE, Crystal Island. The results reveal that learners who strategically game the system perform better than those who use brute-force gaming-the-system strategies throughout gameplay. These findings suggest that gaming-the-system actions can be a form of misdirected regulation. This is important to consider when designing adaptive scaffolds. Scaffolds should not only address maladaptive behaviors but also consider when and how learners act to better encourage adaptive behaviors that support learning.

Completion Date

2026

Semester

Spring

Committee Chair

Azevedo, Roger

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Modeling and Simulation

Document Type

Dissertation/Thesis

Identifier

DP0053121

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