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Record W3133676730 · doi:10.5430/ijhe.v10n4p187

The Impact of Co-Curricular Activity Assessment on Male University Students’ Course Performance: A Case Study of The Natural Sciences Course

2021· article· en· W3133676730 on OpenAlexvenueno aff
Gehan Labib Abuelenain, Muhammad Usman Farooq, Makhtar Sarr

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationCompetence (human resources)ExcellenceCourse (navigation)Mathematics educationPsychologySignificant differenceMedicineEngineeringPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.435
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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