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Record W4390977232 · doi:10.5267/j.ijdns.2024.1.011

Students’ academic performance before, during, and after COVID-19 in F2F and OL learning: The impact of gender and academic majors

2024· article· en· W4390977232 on OpenAlexvenueno aff
Abdoulaye Kaba, Shorouq Eletter, Ghaleb A. El Refae, Abdul Razzak Alshehadeh, Haneen A. Al-Khawaja

Bibliographic record

VenueInternational Journal of Data and Network Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Academic achievementPandemicPsychology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mathematics educationMedical educationMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The main purpose of this study was to investigate students’ academic performance (SAP) before, during, and after the COVID-19 pandemic in face-to-face (F2F) and online learning (OL) instructions. The study also attempted to determine the impact of gender and academic major on students’ academic performance. For the results of semester grade point average (SGPA), the findings of the study showed better SAP in F2F learning as compared to OL learning, while the results of grade point average (GPA) indicated better SAP in OL learning than in F2F learning. The findings supported the stated hypotheses by indicating the positive impact of gender and academic major on SAP in F2F and OL learning, before, during, and after the COVID-19 pandemic. The regression analysis revealed that the demographic variables can predict up to 18% variations in the student’s academic performance. These findings offer valuable insights for practical strategies to improve SAP in F2F and OL learning.

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)0.002
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 score0.228
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.394
Teacher spread0.361 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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