Efficacy of Online Learning on the Development of Students’ Academic Competence: A Case Study of Yemeni Undergraduate Students Studying in India
Bibliographic record
Abstract
This study investigates the efficacy of online learning on the academic competence of Yemeni undergraduate students in India. It examines whether online learning delivers adequate quality education by evaluating students’ academic competence levels. Two groups of Yemeni students participated: 27 Arts students and 35 Science students, from five Indian universities. The Academic Competence Evaluation Scale (DiPerna & Elliott, 2000) was employed to assess students’ competence through online learning, with data collected online and analyzed using SPSS. The scale was used to evaluate Yemeni students’ academic competence through online learning. Descriptive statistics indicated moderate levels of academic competence for both groups. Despite the Arts group showing higher scores, the Independent Samples T-test revealed no statistically significant difference between the groups. Pearson Correlation analysis demonstrated a significant positive correlation between students’ academic skills and academic enablers. Furthermore, Linear Regression analysis indicated that academic skills significantly impacted academic enablers. The findings suggest that online learning can be a viable alternative during Covid-19, provided that certain adjustments are made. This study contributes to the growing body of knowledge on online learning, particularly in developing countries like Yemen, offering empirical evidence for policymakers, educators, and institutions on its effectiveness and areas needing improvement. The results underscore the importance of integrating academic skills and enablers into online learning environments to enhance academic competence among students.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".