Aspects of the implementation of digital tools in the visual arts classroom
Bibliographic record
Abstract
The aim of this research compilation is to show and discuss aspects of the implementation of digital tools in the visual arts classroom in Sweden. The purpose is to find out what approach visual art teachers have to the use of digital tools in their classroom, and how this affects the lessons and interactions between the students and the teachers. The articles have been found through multiple sources, the ones used are ERIC, SwePub and DiVa-Portal. The articles are all peer-reviewed and published after 1996. The main amount of articles are in a Swedish context, but there’s also a few international articles to give a broader perspective. These other articles come from Australia, Canada and the United Kingdom. Some of the articles haven’t been found via the mentioned sites, but were nudged to us by our supervisor. The results show that visual art teachers are still to some degree reluctant to incorporate the use of digital tools in the visual arts classroom. This is mostly due to lack of knowledge, skills or resources available for the teacher. Another part is that the visual arts still are considered a creative hands-on subject, which makes it hard to implement digital tools since they mostly are considered theoretical tools. This research compilation concludes that there’s still further work to be done to implement digital tools in a way that visual art teachers can fully use. And there’s still a need for digital competence among both students and teachers. However, it’s already starting to turn, with more students gaining access to their own personal computers, consequently more opportunities for digital learning appearing.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".