MétaCan
Menu
Back to cohort
Record W4308802884 · doi:10.24908/pceea.vi.15847

Influencing Factors Impacting Women to select Engineering- A Range of Perspectives

2022· article· en· W4308802884 on OpenAlexvenueno aff
Maryam Moridnejad, Wendy Fox‐Turnbull, Paul D. Docherty

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceDiversity (politics)Engineering educationFocus groupPerceptionQualitative researchEquity (law)EngineeringMedical educationPsychologyPedagogyEngineering ethicsPolitical scienceSociologyEngineering managementMedicineSocial science

Abstract

fetched live from OpenAlex

To find solutions to complex problems, engineering practice needs to adapt and embrace diverse thinking. The lack of female participation in engineering fields, in the western world including New Zealand, at the tertiary education level (post secondary/high school level e.g., university, polytechnic, etc.) has been a barrier for diversity, equity and innovation in both the industry and academic professions. Non-diverse professions miss out valuable contributions and new ways of approaching problems that a varied workforce brings. New Zealand needs to mitigate the gender bias to ensure a diversity of skills and knowledge in the engineering profession is fostered. This paper presents findings from three studies as part of a larger research project aimed at investigating influencing factors that contribute to female participation in engineering studies at the tertiary level in New Zealand. In the three studies researchers explored student teachers, current polytechnic engineering students and high school students’ perceptions, experiences, and influences related to engineering.
 The first study investigated future teachers of children aged 11-13 years perceptions of engineering and engineers. The second study investigated the impacts and influences that led domestic and international female engineering students choose the Civil Engineering programme at Waikato Institute of Technology (Wintec). The third study investigated the impacts and influences that led to Year 12 and 13 students to enrol in a trades engineering related course at Wintec. The three completed studies deployed qualitative research methods using focus group and individual interviews.
 The first study found that participants held very strong stereotypical views about who engineers are and described them as: white, male, middle-aged, good at maths and science who may be antisocial, and that they design and build stuff while getting dirty. The second study found that barriers to selection of engineering for women include the school system; lack of career and subject choice guidance available to students at school, lack of promotion of the profession, and society’s perception of engineers as being masculine. The third study found that young women were exposed to strong stereotypical thinking and behaviours throughout their lives that could potentially steer them away from a career in engineering. Other barriers included a lack of timely, accurate career advice, outdated school facilities and inauthentic enactment of curriculum. However, exposure to positive role models and strong support networks, along with developing self-efficacy, assisted them to overcome these barriers enabling them to explore engineering as a potential career pathway.
 Given the strong stereotypical views about engineering from future teachers, incorrect perceptions about engineering in society and lack of engineering career and subject choice guidance available to students at school, it is not surprising that there is a shortage of females entering engineering fields in New Zealand.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicCareer Development and DiversityFrench-language works237,207