Development of a New Framework to Guide, Assess, and Evaluate Student Reflections in a University Sustainability Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it