Incorporating NBIC social/ethical issues into STEM teacher education programmes
Bibliographic record
Abstract
Heightened emphasis is being given worldwide to STEM education at all educational levels. In common with the Caribbean, various challenges regarding STEM education are being noted in other parts of the world, e.g. Canada (DeCoito, Steele & Goodnough, 2016). A critical component of STEM education pertinent both to Canada and the Caribbean is that of STEM teacher education. An ongoing research study in the School of Education at The University of the West Indies, Mona addresses an issue of relevance to Canada and the Caribbean, i.e. culturally relevant/responsive STEM teacher education (e.g. Aikenhead & Elliott, 2010). The study investigates culturally relevant/responsive STEM teacher education within the context of Caribbean secondary level science/STEM teachers’ perspectives and beliefs regarding social/ethical issues associated with nanotechnology-biotechnology-information technology-cognitive science (NBIC) converging technologies (Roco, 2016). Ultimately, the study wishes to provide a foundation for the development of a STEM teacher education degree programme that (i) develops STEM-specific pedagogical content knowledge to guide integrated STEM teaching and learning; and (ii) also develops STEM teachers’ awareness of social/ethical issues associated with a range of 21st-century STEM topics, and concepts. The ongoing study is being conducted utilising a qualitative research methodology. Key NBIC concepts/topics and associated social/ethical issues have been introduced into a course taught in the current MEd Science Education programme. Students are asked to discuss their beliefs, views, and perspectives (verbal and written) regarding these issues, and whether they may or may not choose to incorporate such issues into their secondary level science/STEM teaching. Data regarding these teachers’ beliefs, views and perspectives have been obtained from the course in question over three academic years, i.e. 2017-2018; 2018-2019; and 2019-2020 (course taught once per academic year). Data currently are being organised into representative themes for subsequent analysis. Both Canada and the Caribbean are currently engaged in regional/national efforts to shape the future of STEM education/STEM teacher education (e.g. the Canada 2067 national initiative, https://canada2067.ca/en/; Sweeney, 2019). Given the stated goals of the recently established Canada-Caribbean Institute, the study summarised here becomes significant in the opportunities it holds for collaborative research efforts in STEM education/STEM teacher education.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".