Effects of Collaborative Online Learning on EFL Leaners’ Writing Performance and Self-efficacy
Bibliographic record
Abstract
<p>This study explored the effects of collaborative writing instruction on undergraduate nursing students’ writing performance and self-efficacy beliefs within an online learning system. A single-group experimental study utilized two instruments, the NCEEC (National College Entrance Examination Center) writing grading criteria (the SRCT) and a modified writing self-efficacy questionnaire (the WSQ), was conducted. The intervention was applied in the context of a four-month freshmen semester at the beginning of a two-year vocational education program conducted in fall 2010. Two hundred and nine learners were recruited through convenience sampling from four classes at a nursing vocational university in southwestern Taiwan. Quantitative data were analyzed using descriptive statistics, repeated measures MANOVA, explorative factor analysis (EFA), and structural equation modeling (SEM). The results showed that this instructional method effectively improved the learners’ writing performances and also influenced the latent structures of the learners’ self-efficacy from theoretical constructs toward pedagogical meanings, with the learners’ writing self-efficacy beliefs being altered by the instruction and becoming consistent with the assessment criteria. In addition, both the learners’ pre- and post-test self-efficacy levels had significant causal relationships with their individual learning progressions. These correlations between self-efficacy and writing performance suggest further teaching implications.</p>
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".