<i>Évaluation et multimédia dans l’apprentissage d’une L2</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the first part of this paper different areas where technology may be used for second language assessment are described. First, item banking operations, which are generally based on Item Response Theory but not necessarily restricted to dichotomously scored items, facilitate assessment task organization and require technological support. Second, technology may help to design more authentic assessment tasks or may be needed in some direct testing situations. Third, the assessment environment may be more adapted and more stimulating when technology is used to give the student more control. The second part of the paper presents different functions of assessment. The monitoring function (often called formative assessment) aims at adapting the classroom activities to students and to provide continuous feedback. Technology may be used to train the teachers in monitoring techniques, to organize data or to produce diagnostic information; electronic portfolios or quizzes that are built in some educational software may also be used for monitoring. The placement function is probably the one in which the application of computer adaptive testing procedures (e.g. French CAPT) is the most appropriate. Automatic scoring devices may also be used for placement purposes. Finally the certification function requires more valid and more reliable tools. Technology may be used to enhance the testing situation (to make it more authentic) or to facilitate data processing during the construction of a test. Almond et al . (2002) propose a four component model (Selection, Presentation, Scoring and Response) for designing assessment systems. Each component must be planned taking into account the assessment function.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it