Building confidence in STEM students through breaking (unseen) barriers
Bibliographic record
Abstract
Abstract. Science, technology, engineering, and math (STEM) subjects have historically struggled to be inclusive and accessible to students from diverse backgrounds. The field of geoscience, in particular, has also had challenges in diversity with respect to staff and student recruitment. The consequence of non-inclusive practices still propagates today, with certain demographics not engaging in STEM activities. As a result, there needs to be conscious efforts to adopt equity, diversity, and inclusive (EDI) initiatives for subjects such as geoscience to grow. In this article, we outline the steps we have taken to break down known (and unknown) barriers to education in the teaching of a science outreach course to a diverse student body. Our outreach course, Think Like A Scientist, has been running in a number of English prisons since 2019. Although the programme is tailored to the restrictive prison environment, the application of its core principles to education are fundamental EDI practices that could be beneficial to a wide audience. In this paper, we outline our reasoning for specific pedagogical choices in the classroom when working with students that have low confidence in STEM education, and we highlight the need for engagement that is not only relatable, accessible, and inclusive but also offers encouragement.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".