Leveraging a Campus Equity Walkthrough Evaluation (CEWE) ePortfolio to Assess First-Year Students’ Equity-Minded Learning and Campus Belonging
Bibliographic record
Abstract
Scholarship demonstrates that ePortfolios enable students to collect work over time and reflect upon personal, academic, and career growth. However, a discussion on whether ePortfolios helps firstyear students describe their equity-mindedness and document their campus belonging perspectives remains mostly unexplored. The purpose of this point-in-time, qualitative research study is to describe first-year students’ experiences completing an on-campus physical walkthrough each spring quarter of 2017, 2018, and 2019. All first-year students were enrolled in a yearlong Freshman Inquiry course at Portland State University in Oregon. This study utilizes Saldaña’s (2016) in vivo coding approach to analyze students’ survey responses and summative essays. The research design begins with students answering an anonymous pre-learning survey each spring quarter, then completing an on-campus walkthrough during the same spring quarter utilizing a Campus Equity Walkthrough Evaluation (CEWE) learning ePortfolio and concludes with students writing a summative reflective essay. The study found three themes: (a) Before completing the CEWE, students defined equality and equity interchangeably with fairness; (b) while completing it, students showed surprise at the variety of on-campus student resources; and (c) after completing the CEWE, students identified inclusion and exclusion experiences on campus based on their social identities. The results suggest that the CEWE shifted first-year students’ understanding of equity-mindedness in three ways: (a) First-year students identify racialized structures and practices on campus, (b) the equity-minded ePortfolio framework develops students’ capacity for self-reflection, and (c) students determine that racialized structures and practices on campus impact their campus belonging.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".