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Record W4399682989 · doi:10.54536/ajywe.v3i1.2596

Experiences of Female Undergraduate Engineering Students in Academia and Industry: A Literature Review

2024· review· en· W4399682989 on OpenAlexaff
Lisa Hoang

Bibliographic record

VenueAmerican Journal of Youth and Women Empowerment · 2024
Typereview
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInternshipMentorshipMedical educationVariety (cybernetics)Undergraduate researchPsychological interventionPsychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

Higher educational settings have a slow rise of women in engineering programs. This literature review synthesizes existing research and has intentions to gain further understanding of female undergraduate engineering students—such as perceptions and experiences—on their respective university campus and internship programs through answering the below the main research: What is the nature of experience for female undergraduate engineering students on university campus and internship programs? This literature review provides important insights of needing support programs at early stages for students in their undergraduate education. Early interventions such as informal mentor relationships provide female students with stronger engineering identities for female engineering students. Ten research articles are examined in this literature review. The selection criteria emphasize on primary, peer reviewed articles that had to be in English with recent publications (earliest publication date of 2007) in the engineering or STEM field, the article revolved in higher education settings with students, faculty or staff as participants, article research objectives involve with gendered issues or mentorship programs and lastly, the articles involve with internship or academic support. Although the literature across were all in English, this literature review contains research from other countries—though mainly are from United States—Australia, England, Brazil, Spain and Kazakhstan. The variety of countries included provided consistencies that contextual support and supportive programs are crucial for fostering greater self-efficacy, persistency and resiliency for female undergraduate engineering students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.401
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.341
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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