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Record W3217732399 · doi:10.21432/cjlt27952

Evaluating Teachers' Learning, Perceptions, and Cultural Differences Following Professional Development for Early Literacy Software

2021· article· en· W3217732399 on OpenAlexaffvenueabout
Constanza Uribe-Banda, Eileen Wood, Alexandra Gottardo, Anne Wade, Rose Iminza, Maina WaGĩokõ

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsConcordia UniversityWilfrid Laurier University
Fundersnot available
KeywordsKenyaPerceptionProfessional developmentPsychologyLiteracyTechnology integrationFaculty developmentMathematics educationTeaching methodCognitively Guided InstructionPedagogy

Abstract

fetched live from OpenAlex

The present study examined the impact of professional development training on Canadian and Kenyan teachers’ confidence, comfort, and perceptions of their abilities to teach early literacy skills in the primary or elementary grades. Data were collected prior to and following training on how to integrate early literacy software as part of ongoing in-class instruction. Domain and technology constructs consistent with Mishra and Koehler’s (2006) technology integration model were assessed, as were perceptions related to delivery pacing. Overall, outcomes reflected more similarities than differences across the two groups of teachers. Limitations in foundational knowledge regarding concepts specific to early literacy were evident in both groups, despite higher levels of perceived confidence in Kenyan teachers compared to Canadian teachers in some content areas. Perceived comfort using technology and teaching with technology were highly correlated, with no differences observed across teacher groups. Pacing was perceived to be faster for Kenyan teachers compared to Canadian teachers. Implications for professional development in this domain are discussed.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.343
Teacher spread0.315 · 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 designObservational
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

Citations2
Published2021
Admission routes3
Has abstractyes

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