The Role of Faculty Mentoring in Improving Retention and Completion Rates for Historically Underrepresented STEM Students
Bibliographic record
Abstract
There is a growing recognition of the need for science, technology, engineering, and mathematics (STEM) workers who provide diverse perspectives enabling companies to keep up with the demands of the 21st-century workforce. Creating a diverse workforce requires improving access to STEM education for historically underrepresented students, including low-income students and first-generation students. However, significant challenges and barriers exist. The purpose of this paper is to showcase an innovative approach to mentoring historically underrepresented STEM students which integrates photovoice and photo-elicitation. This new approach in mentoring takes student participation one step further by asking students to document and share their lived experiences through photographs (e.g., photovoice). Then, photo-elicitation is used to further engage students in discussing what led to their subsequent empowerment in leveraging successes or overcoming barriers. The study was conducted with 19 participants who were primarily American Indian students attending a small college in Wisconsin, USA. The findings suggest students benefited from the mentoring program and perceived it as an enriching learning experience which aided in goal development, accountability, and an opportunity to learn more about strategies for student success. The implementation of this new approach and the results gathered from this study are important as they may inform educational leaders and postsecondary institutions serving historically underrepresented STEM students on supports and strategies that could be carried out on their campuses.
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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 | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".