Teaching individuals to conduct a preference assessment procedure using computer-aided personalized system of instruction
Bibliographic record
Abstract
Preference assessments are an evidence-based procedures used to identify potential reinforcers for persons with developmental disabilities. There is a need to develop effective and efficient procedures to teach students and staff to conduct preference assessments, but only a small number of studies have been conducted and only two have used self-instructional materials. A recent study by Ramon et al. (2012) found that a self-instructional manual was more effective than a method description extracted from published articles for teaching university students to conduct multiple-stimulus without replacement preference assessments for persons with developmental disabilities. The present study extended this research by (a) adapting the self-instructional manual from Ramon et al. for online delivery, (b) adding video modeling as a teaching component, and (c) delivering the training package using a modified computer-aided personalized system of instruction (CAPSI, Pear and Kinsner, 1988). The training package was evaluated using a multiple-baseline design across three university students, replicated across three more students; and a multiple-baseline design across a pair of staff members, replicated a across a second pair. During the baseline phase, participants studied a two-page written description of the assessment procedure adapted from published studies. During the self-instructional manual phase, participants completed all of the following online: studied the self-instructional manual presented in eight units, viewed video demonstrations of the procedure, and completed review exercises scored by the computer program to demonstrate mastery of each study unit. Performance accuracy of each participant was scored using a standard behaviour checklist during a simulated preference assessment conducted following each phase. Clear and immediate improvement in performance accuracy was observed in all participants immediately following the self-instructional training package. Overall, students improved from a mean of 35% correct in baseline to a mean of 94% correct following CAPSI and staff improved from a mean of 23% correct in baseline to a mean of 87% correct following CAPSI. During retention and generalization assessments conducted from 7 to 17 days following self-instructional training, five of the six students and one of the four staff members performed at or above 85% correct (the mastery criterion). The findings showed that online delivery of the self-instructional manual plus video modeling has tremendous potential for providing an effective method for teaching a preference assessment procedure without face-to-face instruction.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".