A teacher-verification study of speaking and writing prototype tasks for a new TOEFL
Bibliographic record
Abstract
This study was undertaken, in conjunction with other studies eld-testing prototype tasks for a new TOEFL, to evaluate the content validity, perceived authenticity and educational appropriateness of these prototype tasks. We interviewed seven highly experienced instructors of English as a Second Language (ESL) at three universities, asking them to rate their students’ abilities in English and to review samples of their students’ performance to determine whether they thought seven prototype speaking and writing tasks being eld-tested for a new version of the TOEFL® test: • represented the domain of academic English required for studies at English-medium universities or colleges in North America; • elicited performance from their adult ESL students that corresponded to their usual performance in ESL classes and course assignments; and • realized the evidence claims on which the tasks had been designed. The instructors thought that most of their students’ performances on the prototype test tasks were equivalent to or better than their usual performance in classes. The instructors viewed positively the new prototype tasks that required students to write or to speak in reference to reading or listening source texts, but they observed certain problems with these novel tasks and suggested ways that their content and presentation might be improved for the formative development of these tasks.
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 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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".