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Record W3192875634 · doi:10.21432/cjlt27943

Educational Technology Competency Framework: Defining a Community of Practice Across Canada

2021· article· en· W3192875634 on OpenAlexaffvenueabout
Lyn K. Sonnenberg, Arif Onan, Douglas Archibald

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsCLARITYDelphi methodContext (archaeology)Educational technologyHigher educationTechnology integrationKnowledge managementPedagogyEngineering ethicsSociologyPublic relationsPsychologyComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

Post-secondary institutions need clarity regarding what their educational technology teams can offer. Educational technology is not simply a hammer that can be quickly utilized, but rather an instrument that needs to be tuned for each unique learning context. Using a modified Delphi approach, we validated an educational technology framework that highlights the necessary capabilities, competencies, and example activities needed in higher education across Canada, which moves away from traditional roles and responsibilities. This framework captures the need for teams to educate, collaborate, design, develop, administer, and lead within their institutions. It also highlights the revealed desire and need to create broader communities of practice and collaborations between various institutions. Educational technology teams themselves, when functioning optimally, will not only transform the academic experience for learners and teaching faculty, but they will ultimately shape the direction of higher education’s teaching and 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)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.021
GPT teacher head0.371
Teacher spread0.351 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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