Comparison of error-correction procedures for teaching topography- and selection-based responses
Bibliographic record
Abstract
Discrete-trials teaching is a technique that is commonly used for teaching functional skills to individuals with developmental disabilities. On each trial, a correct response typically leads to a reinforcer while an error leads to a correction procedure. Several studies have compared different error-correction procedures that involved different amounts of practice following an error in teaching different skills such as sight word reading (e.g., Worsdell et al., 2005), math skills (e.g., Rapp et al., 2012), and visual discriminations (e.g., Smith et al., 2006). Although results were mixed, practicing the correct response was generally more effective than no practice for teaching what might be classified as “topography-based” responses, but not for teaching “selection-based” responses. This raises the question: Does the amount of practice following an error interact with the response classes being taught? The present study attempted to address this question by comparing multiple-practice and no-practice error-correction procedures in teaching topography-based (signing or daily living skills) and selection-based responses (2-choice non-identity matching tasks) with 6 adults diagnosed with an intellectual disability and with limited communication skills. The error-correction procedures were compared in an alternating-treatments design to teach topography-based and selection-based tasks within each participant. By excluding 3 comparisons in which the participants did not master any tasks in topography-based training, results on task mastery showed that 4 of the 6 comparisons favored the multiple-practice procedure while 1 comparison favored the no-practice procedure. The remaining comparison showed no difference across procedures. By excluding 1 comparison in which the participant did not master any tasks in selection-based training, results on task mastery showed that 1 of the 4 comparisons favored the no-practice procedure, and 1 comparison showed no difference across procedures. The remaining 2 comparisons favored the multiple-practice procedure. The findings of this study suggest that a multiple-practice error-correction procedure is slightly more effective than a no-practice error-correction procedure for teaching topography-based responses, but not for teaching selection-based responses. If the present results are generalizable, practitioners may wish to use the simpler and less time consuming no-practice procedure for teaching selection-based tasks and reserve the use of a multiple-practice procedure for teaching topography-based tasks.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".