The Use of Art in the Teaching Practice for Developing Communication Skills in Adults
Bibliographic record
Abstract
The use of Art for educational reasons has been recently developing in Greece both in formal education and in Adult Education. Relevant theoretical texts and studies, (Dewey, 1934. Gardner, 1990. Perkins, 1994) pin point that training through the Arts can contribute to an integrated learning, since through systematic observation of works of art, the trainees´ critical thinking, creativity and fantasy can be generated. The first part of the current paper, examines the reasons that necessitate the use of art in the training practice.The models of approaching and understanding art for educational reasons, as presented and analyzed by Feldman, Brondy, Anderson and Perkins, are presented in the second part.The method “Transformative Learning through an aesthetical experiences”, has been grounded and developed by A. Kokkos, and is presented in the third part. The different stages of this method are also analyzed.In the final part, an example of Kokkos´ method is being analyzed (stage by stage), regarding the training for an organization’s staff development of their communication skills. Conclusions regarding the use of art in the training praxis, may be found at the final part of this paper.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".