Student perceptions of reflection and the acquisition of higher-order thinking skills in a university sustainability course
Bibliographic record
Abstract
Sustainability challenges are complex and call for the effective development of knowledge, skills, and abilities in current and new leaders. New offerings in higher education provide sustainability training to complement studies in geography, engineering, science, and other disciplines, in many cases including innovative experiential learning components – including the use of reflection. Although reflection in education is not a new concept, how to assess reflection has remained challenging. Recent research on the Reflective Learning Framework (RLF) aims to address many of the challenges associated with guiding, assessing, and evaluating student learning through reflections. The objective of this research was to investigate the perceptions of students who use the RLF about their experience using reflection in a sustainability course. Semi-structured interviews with students provide a valuable perspective on the use of reflection. Particularly, findings from this research indicate that students see reflection as a tool to develop and use cognitive and metacognitive skills, and also as a tool to support knowledge retention and transfer. Accordingly, student perspectives on reflection show that this practice contributes to the acquisition of higher-order thinking skills required to address the complex challenges of sustainability.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".