What works? What's missing? An evaluation model for science curricula that analyses learning outcomes through five lenses
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
Science is rapidly changing with vast amounts of new information and technologies available. However, traditional instructional formats do not adequately prepare a diverse population of learners who need to evaluate and use knowledge, not simply memorize facts. Moreover, curricular change has been glacially slow. One starting goal for curricular change can be identifying the features of a current curriculum, including potential areas for improvement, but a model is needed to accomplish that goal. The vast majority of studies related to curricular change have been conducted in K-12 environments, with an increasing number in post-secondary environments. Herein, we describe a model for science curriculum evaluation that we designed by integrating a number of different approaches. That model evaluates the intended, enacted, and achieved components of the curriculum, anchored by analyzing learning outcomes through five lenses: (i) a scientific <italic>Framework</italic> reported by the US National Research Council, (ii) systems thinking, (iii) equity, diversity, and inclusion, (iv) professional skills, and (v) learning skills. No curriculum evaluation models to date have used the five learning outcomes lenses that we describe herein. As a proof of principle, we applied the evaluation model to one organic chemistry course, which revealed areas of strength and possible deficiencies. This model could be used to evaluate other science courses or programs. Possible deficiencies may be addressed in other courses, in the course at hand, or may not be deemed necessary or important to address, demonstrating the potential for this evaluation to generate areas for discussion and ultimately, improvements to post-secondary science education.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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