<i>Computer-assisted second language assessment: to the top of the pyramid</i>
Bibliographic record
Abstract
With innovative ways available to assess language performance through the use of computer technology, practitioners have to rethink their preferred strategies of language testing. It is necessary to take into account both the new developments in language learning and teaching research and also the latest features computers have to offer to help with language assessment. In addition to best practices developed over the years in the field, it is necessary for provision to be made for authentic assessments of intercultural communication abilities. After a review of the latest language-testing literature and a discussion of the current problems identified in it, this paper explores the latest developments in computer technology and proposes areas of language testing in the light of the new findings. A practical application follows. This is an adaptation, in a school board in Ontario, of the latest evaluation model. The model represents unit planning as an isosceles triangle with assessed assignments stacked in horizontal bands from the base to the vertex, i.e. the top. The suggestion is offered that this approach can be enriched, by changing the triangle into a pyramid with a different model on each side. Access to the four sides by rotation of the pyramid allows a broader range of activities culminating in one final assessment task at the summit.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".