The Investigating of Self-Regulatory Method to Enhance Students’ Autonomous Learning Ability for Freshmen at Yunnan Normal University
Bibliographic record
Abstract
This study focused on freshmen at Yunnan Normal University. The research objectives were threefold: (1) to investigate the role of self-regulatory methods in enhancing students’ autonomous learning ability, (2) to study the differences between the control group and the experimental group, and (3) to determine if students are satisfied with self-regulatory methods for enhancing autonomous learning ability. The research sample included 9,000 new students in the 2023 academic year. There were 48 classrooms, each consisting of 180-187 students. Students from two randomly selected classrooms were studied. The study employed stratified random quantitative analysis. Statistical analysis used mean average value (X̄), standard deviation (S.D.), and variance to interpret the data. The main research tools were questionnaires, lesson plans, and a satisfaction questionnaire. The results showed: (1) The overall mean score was 4.11 with a standard deviation of 0.4, indicating a generally high recognition of autonomous learning methods among the students in the experimental group, reaching the “agree” level; (2) for the experimental group X̄ was 94.57, while the control group had an X̄ of 75.15. The experimental group exhibited less variability in scores, with an S.D. of 2.82 compared to 9.49 for the control group, indicating more consistent and concentrated performance among the experimental group students. Additionally, the variance for the experimental group was 7.98, whereas the variance for the control group was 90.13, demonstrating the stability and reliability of the experimental group’s performance. (3) By comparing the results of the experimental and control groups, it is evident that self-regulatory methods significantly enhance students’ satisfaction with their autonomous learning experiences. The experimental group had higher satisfaction levels, with an X̄ of 4.71, very satisfied, and an S.D. of 0.23, compared to the control group, which had an X̄ of 2.72, dissatisfied, and an S.D. of 0.71. This study’s results indicate that self-regulatory methods significantly enhance students’ autonomous learning ability, with noticeable differences between the experimental and control groups, and that students are satisfied with these methods. Future research could further explore the application of self-regulatory methods across different educational stages and subjects.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".