Comparing Synchronous and Asynchronous Learning Environments during Process of Learning German as A Third Language in Terms of Enhancing Students' Metacognitive Awareness
Bibliographic record
Abstract
The present study dealt with exploring to what extent asynchronous virtual learning environment (ASVLE) and synchronous virtual learning environment (SVLE) enhance students' metacognitive awareness in L3 German learning process. During a five-week intervention, the students of the experimental group were taught L3 German synchronously through the BigBlueButton web conferencing system. In contrast, course notes, video-recordings were shared on Moodle Learning Management System (MLMS) asynchronously with those of the control group. The study was conducted on 72 undergraduates and assessed based on the pre-and post-tests of the Metacognitive Awareness Inventory (MAI) and semi-structured interviews. The findings indicated that SVLE improved the MAI scores of students in the experimental group. In addition, the findings obtained from the interviews revealed that the interaction between student-instructors in SVLE increased the students' motivation and engagements into the courses compared to ASVLE. As for ASVLE, there was no evidence about the enhancements of the students of control group in terms of metacognitive awareness before and after intervention. Transactional distance and poor quality of the relationship between students and instructor caused students to give up learning L3 German.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".