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Record W2104720858

Promising Practices in Higher Education: Art Education and Human Rights using Information, Communication Technologies (ICT)

2014· article· en· W2104720858 on OpenAlexaboutno aff
Joanna Black, Orest Cap

Bibliographic record

VenueJournal of inquiry and action in education · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyCurriculumPedagogySociologyHuman rightsThe artsTeacher educationAccountabilityVisual arts educationTransformational leadershipSubject (documents)PsychologyMathematics educationPolitical scienceComputer scienceLibrary scienceSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Promising pedagogical practices is described in relation to incorporating ICT (Information, Communication and Technologies) with the study of Human Rights issues in Visual Arts Education for teacher candidates. As part of a course, ‘Senior Years Art,’ students at the Faculty of Education, University of Manitoba during 2013-2014 experienced a class project entitled, ‘digiART and Human Rights: A New Media, Arts Integrated Project.’ For this course the authors drew upon a pilot course held earlier in 2011 as a Faculty of Education Summer Graduate Institute in which significant curricula using new media was connected to the theories outlined in the Human Rights Education Paradigm by Tibbitts (2002) specifically related to the (1) values/awareness model (2) accountability model and (3) transformational model. The authors found that models 1 and 3 relate to pedagogical approaches regarding ICT in visual arts education. In this article the writers will describe one outstanding student’s process learning about ICT in relation to examining the Canadian immigrant experience. The pedagogical approach used has the promise for wider relevance across subject areas.

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)0.000
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 score0.809
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
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 head0.109
GPT teacher head0.364
Teacher spread0.255 · 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
Published2014
Admission routes1
Has abstractyes

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