Education and public engagement using an active research project: lessons and recipes from the SEA-SEIS North Atlantic Expedition's programme for Irish schools
Bibliographic record
Abstract
Abstract. An exciting research project, for example with an unusual field component, presents a unique opportunity for education and public engagement (EPE). The adventure aspect of the fieldwork and the drive and creativity of the researchers can combine to produce effective, novel EPE approaches. Engagement with schools, in particular, can have a profound impact, showing the students how science works in practice, encouraging them to study science, and broadening their career perspectives. The project SEA-SEIS (Structure, Evolution And Seismicity of the Irish offshore, https://www.sea-seis.ie, last access: 6 October 2019) kicked off in 2018 with a 3-week expedition on the research vessel (RV) Celtic Explorer in the North Atlantic. Secondary and primary school students were invited to participate and help scientists in the research project, which got the students enthusiastically engaged. In a nation-wide competition before the expedition, schools from across Ireland gave names to each of the seismometers. During the expedition, teachers were invited to sign up for live, ship-to-class video link-ups, and 18 of these were conducted. The follow-up survey showed that the engagement was not only exciting but encouraged the students' interest in science, technology, engineering, and mathematics (STEM) and STEM-related careers. With most of the lead presenting scientists on the ship being female, both girls and boys in the classrooms were presented with engaging role models. After the expedition, the programme continued with follow-up, geoscience-themed competitions (a song-and-rap one for secondary and a drawing one for primary schools). Many of the programme's best ideas came from teachers, who were its key co-creators. The activities were developed by a diverse team including scientists and engineers, teachers, a journalist, and a sound artist. The programme's success in engaging and inspiring school students illustrates the EPE potential of active research projects. The programme shows how research projects and the researchers working on them are a rich resource for EPE, highlights the importance of an EPE team with diverse backgrounds and expertise, and demonstrates the value of co-creation by the EPE team, teachers, and school students. It also provides a template for a multifaceted EPE programme that school teachers can use with flexibility, without extra strain on their teaching schedules. The outcomes of an EPE programme coupled with research projects can include both an increase in the students' interest in STEM and STEM careers and an increase in the researchers' interest and proficiency in EPE.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".