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Record W4306734993 · doi:10.1080/10573569.2022.2131662

Response to Intervention in Virtual Classrooms with Beginning Writers

2022· article· en· W4306734993 on OpenAlexafffund
Perry D. Klein, Madelyn Casola, Jill Dombroski, Christine Giese, Kristen Wing-Yan Sha, Serena C. Thompson

Bibliographic record

VenueReading & Writing Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpellingTier 2 networkPsychologyResponse to interventionHandwritingNarrativeAttendanceMathematics educationIntervention (counseling)Tier 1 networkPedagogySpecial educationThe InternetComputer scienceLinguisticsWorld Wide Web

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.309
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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