An evaluation of a self-instructional manual for teaching individuals to administer the revised ABLA test to persons with developmental disabilities
Bibliographic record
Abstract
The Assessment of Basic Learning Abilities (ABLA) is a valuable tool that is used to assess the learning ability of individuals with developmental disabilities (DD). The ABLA was recently revised and is now referred to as the ABLA-R. A self-instructional manual was recently prepared to teach individuals how to administer the ABLA-R (DeWiele, Martin, Martin, Yu, & Thomson, 2011). Using a modified multiple-baseline design across a pair of university students, and replicated across four pairs, I evaluated the effectiveness of the ABLA-R self-instructional manual for teaching the students to administer the ABLA-R to individuals with DD. Each student: (a) after studying a brief description of the ABLA-R, attempted to administer the ABLA-R to a confederate role-playing an individual with DD (Baseline); (b) studied the ABLAR self-instructional manual (Training); and (c) once again, attempted to administer the ABLA-R to a confederate (Post-Training Assessment). Participants who achieved at least 90% accuracy in conducting the ABLA-R in their Post-Training Assessment with a confederate then administered the ABLA-R to an individual with DD in a Generalization phase. In Baseline, Post-training, and Generalization phases I scored each participant’s performance using the ABLA-R Tester Evaluation Form. The overall results indicate that the self-instructional manual is an effective method for training individuals to accurately administer the ABLA-R.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".