The Usage of “Quizizz”™ App by Sport Sciences Students in the Bachelor’s Degree Anatomy Lecture and Its Effects on Attitude and Course Success
Bibliographic record
Abstract
Purpose: The aim of this study is to examine the effects of technologically assisted learning for anatomy courses in sport science collegiate. Methods: One hundred forty-four first-year students from sport sciences faculty attended a required anatomy course in the syllabus. The participants of this class were composed of two learning groups as Classical Learning Group (n=48), and New Approach Group (n=96) based on the lecture style. Classical anatomy course carried out with instruction-based method via PowerPoint lecture on the anatomy course materials such as textbooks, models, and printed visualizations of anatomical sites whereas “Quizizz”™ app-based one performed interactively within a technology-assisted way. The end of the semester, participants answered a reliable and valid survey named Anatomy Lectures Attitude Questionnaire (ALAQ). Results: There was a significant difference in the mid-term and course success when compared Classical Learning (CL) and New Approach (NA). However, no significant differences observed final examination, ALAQ results, and sub-factors’. There was a very low correlation between mid-term, final, course success and ALAQ results in NA group. However, no significantly correlation found between mid-term, final, course success and ALAQ results in CL group. Conclusion: The findings reported here suggest that “Quizizz”™ app can enable improving learning outcomes and contribute to test scores for human anatomy in sport sciences collegiate. We do not conceive the substitute traditional learning method within the educational applications for anatomy courses, but it could be regarded as a supplement method of teaching in higher education.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".