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Record W4206340366 · doi:10.18357/otessac.2021.1.1.54

Opening Doors to Open Digital Practice for Educators: The Open Page Project

2021· article· en· W4206340366 on OpenAlexafffundvenueabout
Bonnie Stewart, Nick Baker

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Conference · 2021
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsOpen educational resourcesParadeOpen educationCitizen journalismBachelorBest practiceWorld Wide WebComputer scienceSociologyMultimediaPedagogyPolitical science

Abstract

fetched live from OpenAlex

This paper outlines the design and purpose of an open educational resource (OER) project focused on developing digital literacies and open educational practice (OEP) within a Canadian Faculty of Education. Called The Open Page, the project features a Tool Parade of videos and podcasts created with and by Bachelor of Education (B.Ed.) students). Designed to enable students to build critical and participatory digital literacies with common classroom tools, and to encourage the development of OEP, the project assesses classroom uses of specific educational technology platforms. It also engaged student creators in analysis of various platforms' implications for student data and for differentiated learning. Featured on the University of Windsor Faculty of Education's website, The Open Page and its Tool Parade of OER offer professional development resources for faculty and practicing teachers and contributes to a common conversation about digital learning between educators at all levels. This paper will overview The Open Page and its creation, and the ways in which it represents an effort to focus pre-service teachers on the participatory and production capacities of the web for digital learning.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0140.007
Open science0.0070.005
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 head0.054
GPT teacher head0.384
Teacher spread0.331 · 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 designTheoretical or conceptual
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 routes4
Has abstractyes

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