Bibliographic record
Abstract
This article examines the integration of literature into secondary STEM (science, technology, engineering, mathematics) classes in British Columbia, Canada. Data were collected through interviews with nine secondary STEM subject teachers and focus on teachers’ perceptions of the effects of including literature, what/how literature has been included, as well as the barriers, both real and perceived, to doing so. A review of the literature demonstrates that integrating literature into STEM can be appealing to a broad range of students and teachers and can help to engage students with a variety of interests, perspectives, and backgrounds. The arts, including the literary arts, are a part of STEAM (science, technology, engineering, arts, mathematics) education, which focuses on interdisciplinary or transdisciplinary approaches to education. Furthermore, due to its multiple disciplinary nature, literature can present opportunities for students to learn holistically and help them to better understand the context of the content they are studying. Interview data suggest that literature can also help to make lessons memorable, build community within the classroom, and create opportunities for students and teachers to authentically represent themselves and the subject matter. Participants in this study described several barriers they have faced in choosing to integrate literature in STEM classes, including time constraints, locating appropriate literary material, and managing the expectations of students, colleagues, administrators, and parents. However, the participants in this study all stated that they would continue to include literature in their classes in the future, despite the barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".