Motivating High School Students to Persist in Science through Teacher-Scientist Partnerships
Bibliographic record
Abstract
The improvement of science education has been the focus of numerous studies in the past, however few education researchers have questioned both secondary school teachers and practicing scientist themselves to determine ways in which they could collaborate to make the learning of science a more authentic experience for students. Toward this end, this qualitative study was conducted using semi-structured interviews with secondary school teachers and scientists who were involved in partnerships. During the interviews the following primary research question was addressed: How do secondary school science teachers and practising scientist collaborate to motivate and encourage students to remain engaged in science and/or to pursue careers in science? The data collected was then analyzed to identify common themes. The findings indicate that there are several reasons why students remain engaged in science including types of science-related experiences, family influences, and personal characteristics. Conversely, fear of failure, misconceptions about the nature of science, and time investment were some of the reasons given for the failure of students to persist in science. In addition, although scientists and teachers collaborate in a variety of ways; the availability of resources, teacher apathy, institutional culture and organizational structures act as barriers/hindrances to the formation of these partnerships.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".