The Dynamics of Academic Buoyancy in Contemporary Education
Bibliographic record
Abstract
The study aims to explore the dynamics of academic buoyancy within contemporary education, focusing on how it influences educational outcomes, the mechanisms behind its development, and effective strategies for fostering resilience among students. This narrative review synthesized findings from empirical studies, theoretical papers, and case analyses published between January 2000 and December 2023, using an exhaustive search across multiple academic databases. The selection criteria focused on studies discussing academic buoyancy in K-12 and higher education settings, with thematic analysis used to identify key themes. Research identified predictors of academic buoyancy, such as academic self-efficacy and lower levels of test anxiety, and examined its mechanisms, including its role in students' emotional and cognitive responses to academic challenges. The outcomes of academic buoyancy were found to include enhanced academic performance, engagement, and psychological well-being. Academic buoyancy plays a critical role in students' educational experiences, influencing their ability to navigate academic challenges. Future research should delve deeper into its mechanisms and outcomes, with educational practice incorporating strategies to foster resilience and adaptability among students across diverse settings.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".