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Record W1567449249 · doi:10.19173/irrodl.v12i6.976

Applying constructionist principles to online teacher professional development

2011· article· en· W1567449249 on OpenAlexaffvenue
Nathaniel Ostashewski, Doug Reid, Susan D. Moisey

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsAthabasca University
Fundersnot available
KeywordsConstructionismStrict constructionismProfessional developmentSocial constructionismSociologyPedagogyOnline communityGrounded theoryFaculty developmentRoboticsPsychologyMathematics educationKnowledge managementComputer scienceQualitative researchWorld Wide WebEpistemologyArtificial intelligenceSocial scienceRobot

Abstract

fetched live from OpenAlex

<p>This report explores the first iteration of a teacher professional development courselet grounded in constructionist theory and activities. This online teacher professional development (oTPD) courselet provided opportunities for teachers to engage in just-in-time, ongoing TPD within a social networking site for educators. The topic of the oTPD was Robotics and Hands-on Activities in the Classroom. The courselet was designed for teachers who are interested in integrating constructionist pedagogies into their practice. Key findings of the first delivery of the oTPD courselet point to a need for flexible access, sharing of resources, and support for constructionist pedagogical activities as a PD value for participants. Findings further support the potential for an ongoing online community of practice around robotics in the classroom. The approach taken in this oTPD courselet of study continues to inform a model of oTPD delivery within a social-networking-enabled environment.</p>

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0050.042
Scholarly communication0.0090.009
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.248
GPT teacher head0.506
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations48
Published2011
Admission routes2
Has abstractyes

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