Investigating Nonnative TEFL Students’ Self-Regulation in an Online Learning Environment
Bibliographic record
Abstract
Coping with technological revolution has become unavoidable in the educational process. In addition to the various advantages of integrating technology into the traditional classroom, utilizing it has been compulsory as an inevitable solution to a global crisis such as the Coronavirus pandemic that we face these days. The present study, using a case study design, aims at exploring self-regulatory strategies that undergraduate university students practice while engaging in virtual classrooms. Participants of the study were 187 university students from all levels. They are all majoring in Teaching English as a Foreign Language (TEFL). Data were collected using mixed method approach in which two tools of measurement were used in the research. An online questionnaire was administered to the participants, then online focus group interviews were conducted. Data gathered were analyzed statistically and findings revealed that non-native TEFL students are high-level self-regulatory learners with no significant effect of university level on students’ self-regulation. In addition, pedagogical recommendations were displayed.
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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 | 0.003 |
| 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) | 0.002 | 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".