PHYSICAL PERFORMANCE AND VIRTUAL EDUCATION: TEACHING COMMUNICATION SKILLS ONLINE
Bibliographic record
Abstract
Comparisons between e-learning and face to face instruction are plentiful and even somewhat outdated. However, early studies in efficiency of e-learning are usually constrained to traditional fields such as language, mathematics, science and humanities. Recent research indicates that contemporary information and communication technologies may also be able to offer significant opportunities for education in the field of performing arts such as classical and modern dance [1]. On such basis, authors of this study have tried and implemented e-learning into the highly skills-based field of communication science. This study compares training in communication skills in physical and virtual learning environments. Using a combination of quantitative and qualitative research methodologies – questionnaires and focus groups – it compares various elements contributing to student success in two groups of students at Specialist graduate study in IT technologies at the Polytechnic of Zagreb. The first group of students has attended traditional face to face lectures, while the second group of students has studied independently using online multimedia textbook ‘Communication Skills’ written by Petar Jandric (2012) [2]. Both groups have been surveyed at the beginning, in the middle, and at the end of the semester. Questionnaires and focus groups have been focused to student motivation, expectations from face to face and e-learning classes, average time spent learning, achieved grades and advantages and disadvantages of e-learning. On such basis, this study identifies the main opportunities and challenges for education for physical performance using the contemporary information and communication technologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".