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Record W6888924284 · doi:10.25316/ir-16256

Using evidence-based reading strategies to develop teaching practices: a self-study

2021· article· en· W6888924284 on OpenAlexfundno aff

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersVancouver Island University
KeywordsReading (process)Value (mathematics)Process (computing)Focus (optics)Foundation (evidence)Focus groupOutcome (game theory)

Abstract

fetched live from OpenAlex

This self study investigates a senior high teacher’s use of evidence-based reading strategies and instruction to make changes to planning and teaching, with a focus on supporting students with diverse abilities to have success with reading. Self-study methodology allows teachers to examine their own practice to gain insight, and to grow as both an educator and a researcher. This process both facilitates change and contributes to the educational community. Data was collected through the primary use of written journals, alongside a research log, mind maps/concept maps, visual collages, and critical friend conversations. The outcome of the study provided knowledge for me to create small but meaningful change within my teaching and planning that emphasized the value of intent, purpose, and a foundation of knowledge when focusing on reading strategies and instruction. Graphic organizers, visual mapping tools, paraphrasing, summarizing, explicit instruction, and questioning were key strategies implemented. This study highlights the importance of intentional planning for reading instruction in secondary school classrooms, and emphasizes the value of self-study research for practicing teachers who want to pursue learning, focus on changes they believe in, and become better educators.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.060
GPT teacher head0.326
Teacher spread0.266 · 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.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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