A Comparison of the Tactical Game Approach and the Direct Teaching Models in the Teaching of Handball: Cognitive – Psychomotor Field and Game Performance
Bibliographic record
Abstract
It was aimed in the study to compare the Tactical Game Approach and the Direct Learning Models in Handballtraining, to study the effects in detail on development in the cognitive field, psychomotor field and gameperformance of these models. A total of 43 students who were attending at the Department of Physical Education andSports Teaching, participated voluntarily in this study. Since the participants were attending two different classes,one of the classes was determined randomly as the “Tactical Game Group” (n=25) and the other class as the “DirectLearning Group” (n=18). In the study, a 25-question survey form composed by the experts, was used for measuringthe knowledge levels. A Handball test course was used for determining performances of the psychomotordevelopments of the groups. The 16-item form that contained the offensive and defensive criteria, by taking as thebasis the (GPAI), was used at the stage of game performance assessment. The qualitative data were obtained with theindividual interview technique realized with the Standardized Open-ended Interview Form. Analysis methods wereused for data analyses. In the analysis of the qualitative date, the depiction and analysis stages were followed. Studyfindings stated that both of the learning methods provided development, both in the psychomotor and in the cognitivefield. However, in the game performance field, it was determined that the Tactical Game Approach was moreeffective compared to the Direct Learning Model.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".