Overcoming Problems Integrating Digital Technologies in Contemporary Art Education Practice: Ten Suggestions
Bibliographic record
Abstract
Many art educators are finding it difficult to embrace digital technologies in meaningful and creative ways. Using findings from case study research conducted during 2010-2012, the researcher examined two model high schools and two middle schools located in Central Canada. Data collection involved interviews and participant observation as well as archival collection, and it was found that successful approaches to teaching with digital technologies in art emerged. The author outlines key areas crucial to integrating new technologies well. From the results of the analysis, the following suggestions are provided:(1) focus on digital studio creations (specifically the 'communication' function); (2) implement traditional visual arts as the program foundation; (3) value educators' knowledge in traditional art; (4) integrate new technologies slowly; (5) cultivate traditional project-based pedagogy; (6) embrace power dynamics between teacher and students; (7) break down community walls; (8) show students' work from local to international audiences; (9) foster a critical approach to new technologies; and (10) emphasize digital creativity. In conclusion, educators' practice is shared and examined and through this their direct experiences are discussed. Suggestions are provided for new educators beginning their careers and for more experienced teachers who are finding it difficult incorporating information and communication technology (ICT) creatively in their classrooms.
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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 | 0.054 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".