Combining SLA Theory and Teaching Practice: “Big Bowl of Serial”, or, How to Use TV Series to Become Autonomous Learners of English
Bibliographic record
Abstract
This article deals with an asynchronous, fully online English course titled “Big Bowl of Serial”, which is analysed using Leo van Lier’s ecology of language learning framework (2004). The current scholarly interest in multimodal content and its uses and potential benefits in the classroom (Pattemore/Muñoz 2023) is fertile ground for critically presenting “Big Bowl of Serial” as a case study. The article introduces, firstly, the critical description of “Big Bowl of Serial” (its learning objectives, the theory at its core, and its structure), and, secondly, the analysis of its features in the light of Leo van Lier’s ecology of language learning and its four main constructs – relation, action, perception, and quality (2004). The article is divided into different sections accordingly: after a short introduction, there follows a descriptive section concerning the structure and aims of “Big Bowl of Serial”, after which, the course is analysed using van Lier’s framework (2004); the final section of the article sums up the strengths of the course that emerge from the analysis, and identifies areas that might be further developed in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".