Impact of Changes in Teaching Methods During the COVID-19 Pandemic: The Effect of Integrative E-Learning on Readiness for Change and Interest in Learning Among Indonesian University Students
Bibliographic record
Abstract
The COVID-19 pandemic has forced universities to conduct online learning, requiring lecturers to create innovative e-learning methods and students to be ready to adapt and show high interest in learning. This study aimed to examine the effect of an integrative e-learning method on students’ readiness and interest in learning at Universitas Diponegoro, Indonesia. This research was experimental, designed with one group pretest and posttest, and no control group. As many as 190 students participated, selected using clustered random sampling. Two measurement scales were used: the readiness for change scale and the interest in learning scale. The statistical analysis technique used was a paired sample t-test. The results of paired sample t-test analysis on readiness for change (p = 0.000; p < 0.05) and interest in learning (p = 0.000; p < 0.05) showed significant differences between the pretest and posttest data. The findings indicated that students who participate in integrative e-learning show significant change in the level of readiness and interest in learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".