Developmental Evaluation: six ways to get a grip on the potential of education scholarship to serve innovation
Bibliographic record
Abstract
In March 2020, COVID-19 challenged health and educational systems across the country. The rapid reallocation of resources to ensure public safety had taken priority over educational obligations. Healthcare students were removed from clinical environments as their learning came to a grinding halt. While academic institutions were pivoting and transforming teaching and learning experiences, students responded to the pandemic with innovation, attending to gaps in patient care. As educators, we must understand how we can further support students and faculty to unleash innovative thinking during a crisis. To begin to address this educational need, academic institutions now have an opportunity to broaden the practice of education scholarship in accordance with best practices to nurture innovation and innovative thinking. What framework can aid us in this endeavor? In times of instability, Developmental Evaluation is an approach that can support the implementation of innovations within medical education. Using an example of an innovation in medical education, we offer six practical tips to begin to use Developmental Evaluation to support and enable learners and faculty in the creation of innovations and contribute to a broader definition of education scholarship.
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 | 0.004 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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; a candidate call from one teacher head, 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".