Creative Digital Arts Education: Exploring Art, Human Ecology, and New Media Education through the Lens of Human Rights
Bibliographic record
Abstract
The development of an innovative pedagogical model based on case study research about human rights education regarding discourses of power and food in relation to visual arts education and human ecology education will be examined. The authors outline two ongoing studies about “digiART” and Human Rights: New Media, Art, and Human Ecology Integrated Projects. These projects have been held at the University of Manitoba, Canada for pre-service teachers training to be secondary level educators: the research has been ongoing since 2013. As a result of the studies, meaningful curricula and innovative pedagogy have been developed using contemporary technologies. Key to the studies is not only the incorporation of creative teaching and learning about digital technologies at the higher education level but also integrating human rights issues into curricula. The authors’ approaches to teaching human rights issues to pre-service teachers are described in which they incorporate creative technologies to foster an innovative pedagogical model, and develop productive learning using digital technologies. Student’s new media practices from preproduction to postproduction are delineated and benefits from using this approach are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".