Building Engineering Identity Through Mentorship Vision Boards
Bibliographic record
Abstract
Research on science identity, a framework outlining key dimensions that shape one’s sense of being a “science person” and thus persisting in STEM, ensures learning is not only meaningful but attentive to how identity is shaped, especially for those who experience oppression and exclusion in their STEM environments and worlds more broadly. However, few practical and flexible course interventions exist for bolstering science identity in the classroom. In this paper, we report on a novel intervention to build engineering identity in first-year students: The Mentorship Vision Board (MVB). This paper adopts a thematic analysis to identify salient themes in students' written reflections about their MVB. Our results suggest that the MVB nurtures engineering identity by giving students an active role in carving out a material and conceptual space to reflect on who they are, underscoring the value of reflective writing and arts- based pedagogy in technical fields.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".