Does Blended Learning Reshape Students’ Critical Thinking Skills? An Evaluative Study of Indonesian Learners
Bibliographic record
Abstract
As a consequence of the post-pandemic situation, blended learning has been a new challenge for students as they need to adjust to the new learning model. Such a challenge stems from the requirement to improve students’ critical thinking skills since, in blended learning, they need to adapt to self-directed learning. This present study explores whether blended learning correlates with the critical thinking of Indonesian students majoring in English Literature. The students’ critical thinking skills and the portrayal of the learning engagement in the blended learning system were identified through questionnaires involving 259 students. The teachers’ interviews were also conducted to discuss the plausible model for blended learning. The findings implied that junior and senior students have passed the minimum standard of critical thinking skills and can engage in blended learning systems. Specifically, the senior students have lower critical thinking skills and are less engaged in blended learning than their junior counterparts. It also reveals the weak correlation between critical thinking skills and blended learning system. Thus, the integration of critical thinking materials, ideal proportion of learning styles, and assignments with high-order thinking skills are needed to strengthen the students’ critical thinking skills in the blended learning system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".