Standard prompting and reinforcement versus a multiple-component strategy for teaching visual-visual non-identity matching
Bibliographic record
Abstract
The Assessment of Basic Learning Abilities (ABLA) test can help staff choose training tasks for persons with developmental disabilities (Martin & Yu, 2000). This test assesses the ease or difficulty with which most clients are able to learn six mini-tasks. Most clients who pass level 5 also pass level 6, making level 5 less useful in understanding clients' abilities. A visual-visual non-identity matching (VVNM) task may fall between levels 4 and 6. This study examined whether a VVNM task has one of the qualities of a good milestone task. One quality of ABLA tasks is that a failed ABLA level is difficult to teach using standard prompting and reinforcement procedures. Study 1 examined whether four VVNM tasks could be taught using standard prompting and reinforcement techniques. The participants were two severely and one profoundly developmentally disabled individuals. An attempt was made to teach each participant each of four VVNM tasks, using standard prompting and reinforcement techniques. Two participants learned three tasks, while the third learned two of the four tasks, using this technique. Previous research has also found that, while it is difficult to teach a failed ABLA level using standard prompting and reinforcement techniques, it is possible to do so when using a multiple component technique. Therefore, in Study 2, the multiple component technique was used to teach the participants the tasks that they had failed to learn in Study 1. Participants were the three participants from Study 1, plus a fourth severely developmentally disabled individual. Of the six tasks presented with the multiple component technique, only one was learned. The findings from these two studies suggest that the VVNM task ay not be a good milestone task to replace level 5 of the ABLA test.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".