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.
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 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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".