The Impact of Co-Curricular Activity Assessment on Male University Students’ Course Performance: A Case Study of The Natural Sciences Course
Bibliographic record
Abstract
Our objective was to examine if a co-curricular activity incorporated with assessment methods affected students’ competence in a course. Natural Sciences (201) was chosen as a candidate course for this study. Students’ grade breakdown was examined and analyzed using SPSS and MINITAB software over four academic years from Fall 2012-13 to Fall 2015-16. The number of failed male students was significantly lower (p<0.05) in Fall semesters when compared with the number of failed male students in Spring semesters. A further analysis was attempted as an approach to understand the reasons for the remarkable elevation of success in the Fall semesters. Hence, a questionnaire was given to 121 students and the data showed that the ‘Science Communicators Program’, metaphorically the Science Festival, played a key role in the students’ achievements. The excellence of performance in the Natural Sciences course was detected during the Fall semesters. Thus, this research paper recommends the accommodation of off-campus co-curricular activities in other courses taught at the university.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".