Thesis Format
Monograph
Degree
Doctor of Education
Program
Education
Supervisor
Hibbert, Kathryn M
Abstract
This doctoral study employs Actor-Network Theory (ANT) as both its theoretical framework and methodological foundation to address the underrepresented role of material agency in a human’s learning process in both Game-Based Learning (GBL) and higher education research. By developing an experimental Actor-Network of Learning (ANL) framework - a novel hybridization of ANT (Latour, 2005; Fenwick & Edwards, 2010), the dimensions of network analysis from Leander and Lovvorn (2006) and Knappett (2012) and other scholars - this dissertation reconceptualizes learning as an emergent network effect shaped by the interactions between human and non-human actors. The study adopts a longitudinal case study of “Oswald”, a computer science student, integrating ANT’s relational ontology with empirical data from gameplay observations, interviews, and document analysis (course materials, game instructions) to map learning networks across two contexts: Oswald’s engagement with League of Legends (LoL) and his mandatory university Course L by tracing his learning trajectories presented by the movement of two knowledge actors: ward (in LoL) and K-map (in Course L).
The ANL framework revealed stark contrasts between the two learning networks. In LoL, learning manifested as multidirectional, high-frequency interactions mediated by non-human actors like wards (vision tools) and the Fog of War mechanic. These elements introduced productive uncertainty, fostering adaptive experimentation and feedback loops (e.g., terrain occlusion required iterative ward placement adjustments). Conversely, Course L operated as a rigid, unidirectional network dominated by prescribed curricula (K-maps) and assessment systems that black boxed uncertainty through standardized scores. While LoL’s ward functioned as a dynamic mediator—transforming tactical decisions through intra-actions with game mechanics—Course L’s K-map acted as an intermediary, reinforcing institutional hierarchies and limiting critical engagement until external triggers (internships, self-study) recontextualized its relevance.
Theoretically, ANL bridges cognitive-psychological and sociomaterial perspectives in GBL, demonstrating how non-human actors (e.g., game items, curricula) co-constitute learning trajectories by enabling/constraining agency. Methodologically, the study operationalizes ANT as both a lens and tool, advocating uncertainty-positive design inspired by LoL’s sandbox environment. This challenges performativity-driven educational models, aligning with Barnett’s call for curricula embracing "supercomplexity" through ecological design.
By tracing how uncertainty is translated across networks, the study reconfigures higher education as dynamic ANLs integrating human/non-human agency, emphasizing uncertainty as pedagogical infrastructure. These contributions advance ANT’s application in learning research and propose transformative pathways for curriculum design.
Summary for Lay Audience
This research explores how people learn in two very different settings: playing a popular online game: League of Legends (LoL) and taking a university course (Course L). By studying a computer science student named Oswald over time, the project reveals how learning isn’t just about teachers, books, or personal effort—it’s shaped by a mix of people, tools, rules, and even game mechanics.
The game LoL encouraged Oswald to experiment and adapt. This study focused on investigating how the affordance of game item ward (tools to spot enemies) encouraged him to constantly adjust his strategies. Ward and other game elements created a dynamic, trial-and-error environment where learning felt like solving puzzles in real time. Over a year, Oswald shifted from casual play to competitive gaming, driven by a desire to join higher-ranked matches. His skill improvement wasn’t just about practice; it was about how the game’s design rewarded creativity and persistence.
In contrast, Oswald’s university course (Course L) followed a strict structure. The class had fixed goals, step-by-step lessons, and exams that focused on memorizing and repetitive practices. While Oswald earned high grades, he felt disconnected from the material until a year later, when an internship review and self-learning of coding made him realize how the course’s concepts applied to real-world situations. The rigid structure of lectures and tests had initially made the content feel irrelevant, but time and real-world experience unlocked its value.
The study highlights two key takeaways. Firstly, learning thrives with flexibility. Games like LoL succeed by embracing uncertainty—players learn by experimenting, failing, and trying again. Course L’s strict rules limited this kind of exploration until real-life experiences brought the material to life. Secondly, material elements matter. Features like game mechanics or course settings aren’t neutral—they actively shape how people learn. For example, LoL’s design encouraged Oswald to think critically, while Course L’s focus on exams initially discouraged deeper engagement.
The research suggests that schools and workplaces could create better learning experiences by borrowing ideas from games. Instead of rigid lesson plans, flexible environments that allow experimentation—and connect learning to real-world goals—could help students stay motivated and adapt to challenges. Oswald’s journey shows that learning isn’t just about what a person studies, but how the tools, rules, and environments around that learner either spark engagement or hold it back.
Recommended Citation
Chen, Yi, "Learning in the Fog: Unveiling Learning Networks in League of Legends and Higher Education Through an Actor-Network Theory Lens" (2025). Electronic Thesis and Dissertation Repository. 10763.
https://ir.lib.uwo.ca/etd/10763
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