The Use of Arts through digital technology in Distance Education. The case of the Mental Health Social Work Course.
Bibliographic record
Abstract
This article presents the use of digital technology and visual arts in higher education during the second corona virus lockdown in Greece. The continuous effort to identify creative ways to renew the interest and commitment of students to ongoing distance education led to the engagement of alternative teaching methods via digital means for the course: Mental Health Social Work (7th-semester course of the Department of Social Work at Democritus University). Students were asked to actively participate in an art-based pedagogical activity and successively reflect on thoughts and feelings concerning mental health. They were invited to take a virtual tour of the exhibition entitled “Life on the line”, organized by an NGO producing public multi-faceted art, fundraising, and initiating educational campaigns that focus on mental health, homelessness, and addiction in Canada. It was a campaign aiming to raise awareness of mental health issues through art. Following the virtual tour, students were asked to select a poster (painting or photograph), reflect on it, and note emerging feelings and thoughts concerning mental health. The objectives were to acquire insight to mental health issues through an experiential method utilizing arts. To present a comprehensive study many aspects need to be considered. The light here shines on the theoretical approaches and methods of social work being integrated with visual arts. Qualitative evaluation of a practice that uses arts in the field of mental health in social work took place. The focus is on the connection of data analyzed with theoretical concepts of mental health, as it relates to the pandemic’s influence on conditions and personal experiences. All of this is thoroughly presented, indicating educational benefits for students along with implications for social work education. At the same time, the major contribution of digital technology in Higher Education is highlighted.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".