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Record W4297513749 · doi:10.19173/irrodl.v23i3.6046

Unleashing Adult Learners’ Numeracy Agency Through Self-Determined Online Professional Development

2022· article· en· W4297513749 on OpenAlexvenueno aff
Christopher S. Walsh, Leicha A. Bragg, Tracey Muir, Greg Oates

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2022
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsnot available
Fundersnot available
KeywordsNumeracyAgency (philosophy)Resource (disambiguation)LiteracyPsychologyProfessional developmentExperiential learningMathematics educationPedagogyComputer scienceSociology

Abstract

fetched live from OpenAlex

Opportunities for self-determined online professional development (OPD) are emerging, but their potential for increasing adult learners’ agency is not yet fully realised. Faced with the problem of successfully designing a self-determined comprehensive evidence-based online numeracy resource for educators who are often time poor and do not engage with online learning unless they are intrinsically motivated, we engaged in design research to conceptualise the Birth to Level 10 Numeracy Guide for educators and families. The Birth to Level 10 Numeracy Guide fosters educators’ and adult learners’ numeracy capability across numeracy focus areas from birth to level 10 (16-year-olds). This extensive OPD resource incorporates consistent design elements, double-looped learning, nonlinear learning, self-reflection, and metacognition activities to foster educators’ pedagogical content knowledge (PCK) through experiential learning. With a section dedicated to families, the resource provides suggestions and advice to parents and carers on everyday, authentic activities to develop children and young people’s numeracy understandings at home and in the local community. As education systems continue to grapple with the disruption brought about by the COVID-19 pandemic, the Birth to Level 10 Numeracy Guide is a timely, freely accessed, viable, and scalable option for providing low-cost OPD.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.004
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 head0.073
GPT teacher head0.438
Teacher spread0.365 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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