MOOCs as environments for learning spoken academic vocabulary
Bibliographic record
Abstract
Massive Open Online Courses (MOOCs) are easily accessible for anyone in the world to study any given subject, often for free. However, there is some question as to whether they are comparable to their real-world counterparts. The Academic Spoken Word List (ASWL) created by Dang, Coxhead, and Webb (2017) was designed to create a word list that is more representative of spoken academic English. To contrast the real-world academic context to MOOCs, we created a MOOC academic corpus and compared it with the Michigan Corpus of Academic Spoken English (MICASE). Last, we used both to test the effectiveness of the ASWL. Overall, we found that the ASWL had similar coverage in both the MOOC and MICASE corpora but interestingly saw slightly more coverage in the MICASE dialogic sections. We believe future research should address the slight discrepancy between dialogic and non-dialogic academic situations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".