Analyzing the Effects of Metacognitive Regulation Enhances Students’ Authentic Writing Learning Performance in a Web-based Constructivist Learning Environment
Bibliographic record
Abstract
Inadequate writing skills can prevent learners from improving their writing performance and interfere with their subsequent writing performance in authentic scenarios.The article's research focuses on the effects of metacognitive regulation on students' authentic writing performance in a web-based constructivist learning environment, which relies on constructivist learning environments to better present the authentic writing problems learners face in their studies and lives.In this paper, we adopt the method of randomized group sampling to conduct a single-group pre-test and post-test experiment on 40 students in a public high school.It also chooses students' writing learning achievement as the dependent variable, and students' metacognitive regulation level and writing selfefficacy as the independent variable and mediator variable, respectively, and explores the degree of influence of metacognitive regulation level on students' writing learning achievement through multiple linear regression.The results showed that there was no significant difference between pretest 1 and pretest 2, while posttest 1 and posttest 2 were much higher than pretest 1 and pretest 2. There was a significant positive effect of students' level of metacognitive regulation on students' learning achievement in writing (0.459), and there was a significant mediating effect of students' writing selfefficacy between students' level of metacognitive regulation and students' learning achievement in writing.Relying on the web-based constructivist learning environment can significantly enhance students' metacognitive regulation level and provide a new teaching path to promote students' writing learning achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".