The effect of research on the perceived quality of teaching: a cross-sectional study among university students in Lebanon
Bibliographic record
Abstract
Abstract Introduction The complementarity between research and teaching is still debated, although several higher education institutions require instructors to do research. In the absence of a consensus on this matter and given the lack of related studies in Lebanon, this study aimed to describe students’ perception of research integration into teaching, and to link research and professional skills to quality teaching, using validated scales related to these concepts. Methods A standardized questionnaire was diffused to university students; it included validated scales: the Student Perception of Research Integration Questionnaire (SPRIQ), the Adapted-Teachers’ quality assessment questionnaire (A-TQAQ), the Student Evaluation of Teaching short form (SET37-QS), and Knowledge and Attitudes Towards Health Research Questionnaire. Results Research integration was well perceived, and teaching was well evaluated by 445 participants from various disciplines, particularly those of a higher socioeconomic level, majoring in health, and females, as indicated by their mean scores. Research-active instructors had a significantly better-perceived teaching quality (17%; p < 0.001) than their non-active counterparts. This finding was particularly true among postgraduate and higher GPA students. The multivariable analysis showed that the knowledge and attitude towards research were related to students’ better perception of research integration and higher evaluation of teaching. Conclusion This study showed an overall good perception of research and teaching evaluation among participants from various disciplines, with research-active instructors having a better-perceived teaching quality. These findings could guide decisions on research integration into curricula using multidisciplinary methodologies to strengthen research integration and involve students in research activities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".