Development of a New Framework to Guide, Assess, and Evaluate Student Reflections in a University Sustainability Course
Bibliographic record
Abstract
Many institutions of higher education increasingly place a focus on various forms of experiential education. While much work has been done in this and related areas, the resources currently available are not sufficient to effectively guide, assess, and evaluate student learning. Personal reflections can be used as a tool to assess student learning through experience. However, guiding students through the process, assessing their work, and providing an evaluation presents challenges for educators. A new framework, a robust rubric, and a guide that students and evaluators can use to support experiential learning through reflection are provided. The framework and resources are based on a grounded investigation of student reflections, which were compared to various evaluation models from the literature. The resources discussed in this paper have already been used in practice for over four years and with over 1,000 students. The purpose of this paper is to describe the journey leading to the development of this framework, to provide a description of the rubric and guide, and to share the lessons learned. This framework and accompanying materials will hopefully be a useful resource for instructors and students wishing to support reflection and experiential learning.
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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 | 0.044 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".