A virtual adaptation of the taped problems intervention for increasing math fact fluency.
Bibliographic record
Abstract
In response to restrictions on visitors within school buildings during the COVID-19 pandemic, the evidence-based math fact fluency procedure known as the taped problems intervention was adapted for use in a virtual setting. The present study used a multiple-probe across participants design to evaluate the effects of the adapted intervention on the subtraction fact fluency of three elementary school students with varying degrees of math difficulties. Researchers also measured whether fluency gains would generalize to subtraction fact family problems that were not targeted within the study procedures. Visual analysis of results indicated math fluency improvements across all students, regardless of initial performance level, but no evidence of generalization effects for any participant. Additionally, to further investigate intervention effects, two effect size measures were calculated (WC-SMD and NAP) and each participant's rate of improvement was measured in two ways. Slopes (digits correct per minute [DCM] gains per session) of baseline and intervention phases were compared, and DCM gains per intervention time were investigated. Discussion focuses on implications for providing academic interventions in virtual learning environments, the importance of direct instruction for subtraction fact fluency, as well as future directions for researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".