Response to Intervention in Virtual Classrooms with Beginning Writers
Bibliographic record
Abstract
During the COVID pandemic, two virtual classes of Grade 1 students learned to write personal narratives in a Response to Intervention framework. Classroom teachers delivered Tier 1 Self-Regulated Strategy Development (SRSD) in personal narrative writing to 67% of students. A research associate provided Tier 2 SRSD instruction in personal narrative writing, with reteaching, support and feedback, and ad hoc remediation of handwriting and spelling, to eight students. Then, because three of the Tier 2 students were frequently absent or disengaged, the research associate delivered Tier 3 individual instruction to them. Students in both Tier 1 and Tier 2 instruction made large, statistically significant gains in text quality. Tier 3 students did not make significant gains. Partial correlations, observations during teaching, and teacher interviews suggest that attendance, spelling level, and discourse knowledge affected learning. Teachers identified strengths and limitations of SRSD, Response to Intervention and the specific materials used. The results indicate that in an online setting, SRSD, delivered in a brief RTI format, is effective for improving written expression for typically developing beginning writers and some struggling writers, but that some struggling writers require additional intervention.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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 | 0.001 |
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".