Guidelines for developing and updating short courses and course programs using the ISCB competency framework
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
<strong>Competency frameworks have proved to be a powerful tool for curriculum development and assessment across many subject domains, and the field of computational biology is no exception. Efforts from the ISCB to develop and successively refine a set of competencies for bioinformatics education and various associated mapping tools have provided a framework for bringing competency-based design principles broadly to education and training of a wide range of professionals in need of some level of mastery of the principles and practice of computational biology. This document seeks to provide some basic guidance for education and training professionals in the field in how to use this framework effectively. It includes a basic background on competency-based education and the history of the ISCB competency framework specifically, leading up to the Version 3 framework considered here. It then follows with some basic principles of applying competency-based education and an illustration of how they apply to different tasks in curriculum development. Appendices and various linked documents provide further elaboration and helpful guidance on the ISCB competencies specifically and some ways in which versions of them have been used already to develop diverse forms of bioinformatics education and training experience. Our target readerships are trainers and educators working in computational biology or more broadly in the molecular life sciences, medicine, and other disciplines that use biomolecular data, including those working in academia, industry and the public sector. </strong>
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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