WIP: Centering African American Culture in Engineering Education: Impact of an Africancentered Pedagogy and Curriculum
Bibliographic record
Abstract
This work-in-progress research paper describes and presents preliminary results for an approach to inspire African American participation in engineering. This approach utilizes an African-centered pedagogy and curriculum to support and enhance students' interest, knowledge and self-efficacy in engineering. The implementation and efficacy of this approach are currently being studied through the National Science Foundation (NSF) Racial Equity in STEM Education program. This work is guided by following research questions: In what ways does an African-centered pedagogy and curriculum influence the construction of engineering knowledge? and how does an Africancentered pedagogy impact student learning of engineering concepts and engineering identity formation for urban African American learners? The study was deployed through the Conscious Ingenuity program at Montebello Elementary/Middle School in Baltimore City. Participants completed pre- and postprogram surveys. Mid-program interviews with students and parents were conducted which provided additional insights. Data analysis indicated that the African-centered approach to teaching engineering effectively increased participants' interest, confidence, self-efficacy, and knowledge in STEM, particularly engineering. This paper describes African-centered pedagogy and curriculum, discusses the research methods employed to assess their impact, and presents the study's initial quantitative findings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".