Learning in the Fog: Unveiling Learning Networks in League of Legends and Higher Education Through an Actor-Network Theory Lens
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.