Teaching Observational Learning to Children with Autism: Pedagogical Advancements for the Scientist-Practitioner
Bibliographic record
Abstract
Observational learning is an important skill for all children to acquire. Children with autism often do not demonstrate this skill nor do they learn it on their own. The present study, using a multiple baseline across participants, single case, research design, investigated the effects of using a peer-yoked contingency game with four male participants with autism, aged 4-7 years. Each participant was presented with a simple labeling task while his friend was seated beside him. Participants had the same partners throughout the treatment. Once the model response was emitted, the teacher presented the same task to the observing boy. Data were collected on correctly observed and emitted responses during the game. Pre- and post probes and tests were conducted for observational learning, generalized imitation, and learned reinforcement for peers. Results from this study provide support for the use of the peer-yoked contingency game as a method for increasing observational learning in children with autism. All four participants increased their correct responding to specific tasks and increased their demonstration of observational learning in a natural educational setting. Evidence of increased interest in peers was also observed. The present study provides support for the use of the peer-yoked contingency game to teach observational learning.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".