I Suck at This Game: “Let’s Play” Videos, Think-Alouds, and the Pedagogy of Bad Feelings
Bibliographic record
Abstract
This article explores the pedagogical usefulness of “Let’s Play” videos (LPs), a wildly popular paratext in which video gamers record and narrate their gameplay. I designed and implemented an LP creation assignment in two English Literature classes that focused on digital children’s literature and culture. I imagined my LP assignment as a variation on a cognitive “think-aloud” activity, wherein students and/or instructors vocalize their approach to solving a particular problem. I was curious how these habits of mind might differ when students engage with interactive digital texts as opposed to print literature. What this study exposed is the centrality of feelings—in particular, “bad” feelings like anxiety and frustration, and the silences that often accompany these feelings—to the initial stages of critical thinking. When students contemplated bad feelings and their origins, eventually they were able to offer incisive analyses of their digital texts. Ultimately, this study argues that cognitive and affective “think-and-feel-aloud” activities such as the LP exercise, which allow students to dwell momentarily in bad feelings and silence, create rich teaching and learning opportunities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".