Defining Scientific Competencies for Use in the Classroom
Bibliographic record
Abstract
The purpose of this research is to define cross-curricular competencies with a scientific perspective. In a scientific context, competencies relate to an individual’s ability to comprehend foundational knowledge and solve complex problems to understand natural phenomena. Public education is shifting towards Competency Based Education (CBE) to promote holistic aptitudes that can be carried through life. This paper uses a scientific perspective to identify and define competencies that reflect scientific thinking. This systematic literature review draws upon the cross-curricular literature as a platform to search and define scientific competencies. We have summarized and interpreted the perspectives from junior and senior scientists with regard to the competencies required for scientific endeavours. Further, we have aligned these scientific competencies to the Alberta Education (AE) competency framework to provide context for educators. As a result, we have identified scientific competencies that align with the AE competency framework including: identity, knowledge transfer, collaboration, observation and logical reasoning, systematic research and experimental design, interpretation, and scientific integrity. Developing well-defined scientific competencies is a significant step towards developing the curriculum necessary to prepare students for many emerging scientific discoveries, technologies, and controversies.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".