The Effect of Instructional Pacing on Skill Acquisition and Maintenance for Children with Developmental Disabilities
Bibliographic record
Abstract
Discrete trial training is an instructional method based on the principles of applied behaviour analysis where skills are taught in discrete units. This instructional method has empirical support for increasing skills among children with developmental disabilities. Instructional pacing has been identified as a key variable in discrete trial training that may enhance skill acquisition. Instructional pacing is the rate at which each individual presentation of the instructional target occurs. Research examining the effects of varying the pace of instruction has produced inconsistent findings. This study sought to examine the effects of five paces of instruction on skill acquisition for young learners. Pace was manipulated by varying the interstimulus interval. Two children with a diagnosis of autism spectrum disorder and one with Down syndrome participated in the study. Instructional targets, the specific behavioural skills to be taught to the participants, included: tact: (expressive labelling—i.e., responding to a particular object or event or property of an object or event), listener responding (responding to an instruction), and intraverbal skills (responding to social questions). In contrast to earlier research, participants achieved mastery by demonstrating a previously determined level of skill without prompting in the fewest number of trials in the slowest pace condition. The pace of instruction associated with the fewest minutes to mastery, or most efficient pace, varied across participants. Skill maintenance also varied across participants. Results suggest that the optimal pace of instruction may vary across individuals. Implications for determining the optimal pace of instruction in discrete-trial training with young learners are discussed.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| 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 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".