Exploring Student Experiences and Factors Influencing Teaching and Learning Quality in Inner Mongolian Universities, China
Bibliographic record
Abstract
This study investigates the student experiences and factors influencing teaching and learning quality in Inner Mongolian universities, China. Through a mixed-methods approach combining surveys and interviews, data were collected from a diverse student population across multiple disciplines. The analysis revealed several key findings: students' perceptions of teaching quality were significantly influenced by factors such as faculty expertise, teaching methods, and classroom environment. Similarly, factors affecting learning quality encompassed curriculum design, access to resources, and academic support services. Cultural influences and regional characteristics were also observed to play a role in shaping student experiences. The implications of these findings are discussed in relation to enhancing teaching and learning effectiveness in Inner Mongolian universities, with recommendations for improving pedagogical practices, curriculum development, and support services to promote a more enriching educational environment. A dedicated research team ensures meticulous data collection through well-designed questionnaires by reflecting the distinctive nature of higher education in Inner Mongolia. The study includes 347 students and 336 teachers, employing SPSS for data analysis. Key findings underscore the significance of student-centered approaches, effective teaching strategies, and the alignment of quality assessments with diverse student needs. The research contributes valuable insights for educational institutions and policymakers, emphasizing the importance of supportive learning environments to prepare students for the dynamic global landscape.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".