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Record W4281295148 · doi:10.5539/hes.v12n2p168

Comparing Synchronous and Asynchronous Learning Environments during Process of Learning German as A Third Language in Terms of Enhancing Students' Metacognitive Awareness

2022· article· en· W4281295148 on OpenAlexvenueno aff
Ahmet TANIR

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionGermanPsychologyAsynchronous communicationMathematics educationEducational technologyQuality (philosophy)Intervention (counseling)Computer-mediated communicationMedical educationCognitionComputer scienceThe Internet

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.441
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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