Educating in Uncertainty: Research on Teaching and Learning as a Horizon for Universities in the 21st Century
Bibliographic record
Abstract
The twenty-first-century university faces unprecedented complexity, uncertainty, and diversity, driven by social, technological, and cultural transformations that demand the preparation of professionals capable of addressing both global and local challenges. This article examines how the integration of research on teaching and learning can serve as a strategic axis for higher education institutions to respond to these challenges. Adopting a conceptual and analytical approach, this study synthesizes evidence from the Scholarship of Teaching and Learning (SoTL) and the Science of Learning to explore their complementary roles in shaping effective pedagogical practices. The findings indicate that research-informed teaching strengthens reflective competencies, promotes pedagogical innovation, and supports teachers’ professional development, while also informing curriculum design and fostering inclusive, adaptive, and collaborative learning environments. For students, this integration leads to deep and meaningful learning, fostering critical thinking, collaboration, and self-regulation skills. The significance of these results lies in the fact that aligning evidence-based teaching and learning strategies with institutional goals enables the university to operate as a dynamic ecosystem where research informs practice and learning, in turn enriching inquiry. This approach safeguards against misinformation and shallow learning, prepares graduates to navigate complex professional and social realities, and positions the university as a resilient and socially relevant model.
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 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 | 0.075 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".