An Investigation of Simultaneous Prompting to Teach an Addition Algorithm to Preschool Students
Bibliographic record
Abstract
This manuscript reports the results of an investigation of the simultaneous prompting procedure to teach a five-step algorithm for solving three addition basic facts. Four preschool students receiving Tier 1 services in their school’s multi-tier system of supports (MTSS) framework participated. A multiple probe across participants single case design was employed, and a visual analysis of the data indicate a functional relationship between the independent and dependent variable. Three of the participants achieved the mastery criterion and maintained the targeted learning outcome for 2–6 weeks. These participants applied the five-step algorithm to three sets of novel addition facts and acquired incidental information consisting of mathematics terminology. The fourth participant voluntarily discontinued her involvement in the study but, prior to her withdrawal, her data were similar to that of the other three participants. Social validity data from the participants’ teachers confirmed their satisfaction with the study’s procedures and participants’ learning outcomes. The results extend those from related research and validate simultaneous prompting’s effectiveness in teaching mathematics to students receiving Tier 1 and Tier 3 services in an MTSS framework. Topics for future research are discussed, and include investigating the effectiveness of simultaneous prompting to teach mathematics across students receiving services through all MTSS tiers.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".