Factors Affecting Online Teaching and Learning among Chinese High School Students: Education Equality Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online learning is significant to promote education equality in high school sector. This research aims to explore the factors affecting students' acceptance of online learning, construct a structural equation model of high school students' online learning behavior, and propose measures to promote educational equity. The study employed quantitative research methods, utilizing online questionnaires to gather 633 data from high school students in Dazhou, Bazhong, and Liangshan regions. A comprehensive approach to data analysis was adopted, including descriptive statistical analysis, reliability and validity tests, confirmatory factor analysis, structural equation modeling, and path analysis. Key findings revealed the significant influence of online teaching quality and course content on students' perceived usefulness, ease of use, subjective norms, attitudes towards online learning, and their subsequent learning intentions and behaviors. The study confirmed the mediating roles of these perceptions and attitudes in shaping students' engagement with online learning platforms. In conclusion, the research provides vital insights into the dynamics of online education in a high school setting. It highlights the need for enhanced teaching quality and course design to improve online learning experiences. The findings offer valuable implications for educators, policymakers, technology developers, and other stakeholders, emphasizing the importance of a collaborative approach to create more effective and equitable online learning environments. This study lays a foundation for future research and strategies aimed at optimizing the potential of online education, ensuring it is accessible and beneficial to all students.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it