Factors Affecting Online Teaching and Learning among Chinese High School Students: Education Equality Perspectives
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".