Fostering resilience among university students: the role of teaching and learning environments
Bibliographic record
Abstract
Abstract Resilience, the capability to recover from adversities and adapt to challenges, is essential for university students to succeed academically, personally, and socially in the competitive landscape of the twenty-first century. Much of the prior research has explored the role of individual psychological factors in resilience. However, resilience does not develop within a vacuum and is strongly shaped by the context. Hence, studies that only focus on individual psychological factors might present an incomplete picture, ignoring the role of the higher education environment. This study focused on the potential role of university teaching and learning environments in fostering resilience. We employed an explanatory sequential mixed-methods design to investigate their associations. The quantitative study analyzed data from 1,068 university students through structural equation modelling. We found that students who engaged in more active learning activities and whose teachers provided them with clear goals and standards were more likely to be resilient. The qualitative study was designed to better understand the underlying mechanisms behind the association between teaching and learning environments and student resilience. Through in-depth interviews with 15 university students, the qualitative findings demonstrated how various aspects of teaching and learning environments contribute to the development of resilience. Additionally, individual coping strategies and peer support emerged as key elements that shaped resilience other than teaching and learning environments. These findings underscore the crucial role of enhancing teaching and learning environments, helping students develop coping strategies, and leveraging peer support to foster university students’ resilience.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".