"Games are lit”: The affective and pleasurable reading of narrative-based video games as complex texts and implications for English language arts curriculum and pedagogy
Bibliographic record
Abstract
The past two decades have seen a steady decline in reading motivation, performance, and enjoyment in adolescent learners as they progress through secondary school. This has been attributed to current majoritarian tendencies in English Language Arts courses that honour archaic notions of literacy and literary text, and fail to instill a love of reading and lifelong reading habits in young pupils. Exploring an alternative to these common approaches, this thesis research seeks to delineate the ways in which narrative-based video games can be read as complex, multimodal texts to foster affective engagement and opportunities for pleasurable reading. Meeting weekly over the course of a two month-long period at a youth center in Montréal, Canada, study participants played through Persona 5 (2016): a video game largely centered upon complex storylines, narrative-based technique, and themes commonly found in young-adult literature. With an affect-based perspective and relational materialist methodological approach employed, data were examined according to the unit of analysis of felt focal moments. These methods captured the ways in which meaning-making with new media was an ever-evolving, unpredictable, and embodied process allowing collaborative and felt literacy events to come into being. Findings were utilized to inform an effective and affective pedagogy serving as a tool for English Language Arts educators to better harness the tremendous potential for engaging adolescent students with narrative-based video games. This thesis study addresses a gap in existing research and sets a precedent for future studies to expand knowledge of meaning-making and engagement with new media texts in educational settings
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".