Students’ Readiness and Perception on the Effectiveness of Online Education Post Covid-19 Pandemic
Bibliographic record
Abstract
This research reveals students' readiness and perceptions of online learning post the COVID-19 pandemic. Online learning has gained significant popularity during the COVID-19 pandemic. This learning method offers flexibility and convenience for students, however now that learning in higher education institutions has returned to normal, how students' perceptions of online learning are fully debated in this study. Three hundred and eight students from local universities in Indonesia took part in a quantitative study to explore the satisfaction and effectiveness of online learning. This research is a cross sectional study completed by making an association between various parameters using an independent t-test and one-way ANOVA. The findings report that most of the students are dissatisfied with online learning and only the facts find that it is effective for their learning. It is based on the facts that both genders are equally ready for online learning after the post Covid-19 pandemic. It is proved by Welch test indicating that there was no significant difference in readiness for online teaching between gender (t= 0.089, p = 0.935). There is no significant difference in gender in their perception on the effectiveness of online learning. A negative t-value indicates (t = -0.45, p = 0.653). And there is no significant difference among the students from different faculties in their readiness for online learning based on one-way ANOVA test showing that p<0.05 level on the effectiveness of online learning post-covid [F(5, 127) = 3.714, p =0.004] and the readiness for online learning was not statistically different between faculties [F(5, 127) = 0.827, p =0.533]. Refer to online learning. The study concluded that a fully online mode may not be suitable for all types of learners. Instructors and institutions need to recognize these issues and provide appropriate supports and solutions to improve the effectiveness of online 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".