Fostering STEM identity through storytelling: links to belonging, self-efficacy, classroom climate, and lab performance
Bibliographic record
Abstract
This study explores how integrating an approach to storytelling, called contextualized storytelling, into the laboratory classroom may be related to students’ self-efficacy, sense of belonging, classroom climate, and lab performance. Contextualized storytelling is designed to help students connect academic content to their lived experiences through personalized narratives. Depending on the course learning outcomes, students shared their stories in written and multimodal formats. Using a mixed-methods case study design, data were collected from 105 first-year students enrolled in General Chemistry I and II through pre- and post-course surveys, storytelling artifacts, and semi-structured interviews. Quantitative findings revealed that storytelling reflection, scientific accuracy, and effort were significantly associated with higher levels of self-efficacy, and all three dimensions positively correlated with both story-based and traditional lab grades. Storytelling creativity also showed a modest positive relationship with students’ perceived improvement in disciplinary belonging. A t -test revealed that women scored significantly higher than men in scientific accuracy and storytelling grades, suggesting gender-based differences in narrative engagement. In addition, while General Chemistry II students achieved higher academic outcomes overall, General Chemistry I students demonstrated stronger personal connections in their storytelling, pointing to distinct affective engagement across courses. Interview data identified effort, personal connection, and group sharing as the storytelling features students found most meaningful to their learning. Together, these results suggest that storytelling connects academic engagement, reflective thinking, and STEM identity development while contributing to inclusive and supportive learning environments. This research offers practical guidance for post-secondary instructors aiming to enhance assessment quality and student connection through narrative-based pedagogy.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".