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Record W3109270888 · doi:10.18260/1-2--32961

Increasing the Interest of Elementary School Girls in STEM Fields Through Outreach Activities

2020· article· en· W3109270888 on OpenAlexaffabout
Jennifer Bastiaan, Roger Bastiaan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOutreachWorkforceMathematics educationEngineering educationEvent (particle physics)PsychologyMedical educationEngineeringPolitical scienceMedicinePhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Despite the known value of a diverse Science, Technology, Engineering and Mathematics (STEM) workforce, women and minorities continue to be under-represented in these fields. Engineering undergraduate degrees, in particular, are awarded to women engineering students in the United States and Canada at a lower rate compared to their male counterparts. For the past 20 years, less than 20% of engineering degrees have been awarded to women students, and this stubborn trend is not changing much. The outcome is worse for black and Hispanic students, who usually comprise less than 10% of engineering graduates. Research has shown that low self-confidence in learning math and science subjects starts at a young age in girls and minority students, often in the early years of elementary school, and this ultimately leads to low interest and enrolment in STEM undergraduate programs. In an attempt to combat negative stereotypes about the capabilities of girls and minorities in STEM studies, which undermine the confidence of these groups, the Society of Women Engineers (SWE) has instituted the Girls’ Engineering Exploration (GEE) day. This is an annual STEM outreach event for girls in the Detroit Public School (DPS) system, which is 95% black and Hispanic. GEE is an all-day event for 4th to 6th grade female DPS students. Groups of girls participate in the event with volunteer mentors who are female engineers working in local industry, thus providing the girls with role models. The groups of girls and their mentors cycle through a series of STEM activities that are meant to be engaging, and to increase their interest in STEM careers. In this work, two GEE activities recently created and presented are described in detail. The first activity is a traditional engineering exercise involving physical creation and observation of electrical circuits. The second activity is a novel exercise focused on the new discipline of autonomous vehicle design. The girls experiment with “doodle track cars”, which are inexpensive toy cars that stand in for self-driving vehicles. The toy cars are equipped with optical sensors which enable them to follow hand-drawn lines that represent the roadway. This activity allows the girls to investigate the limitations of real sensors. All of the materials for both activities are provided as educational resources, including science sheets and worksheets, such that pre-college educators can take advantage of these activities in their own classrooms and outreach events with little to no modification. Detailed information about the design and deployment of these activities is reported, including cost of materials and opportunity cost, in terms of time invested in preparing the activities for students. Furthermore, the results of student surveys from GEE, in the form of questionnaires for each activity, are analyzed and presented. The conclusion is that modern topics such as autonomous vehicles are well worth the activity development effort, as students are more engaged in these activities than in derivative exercises such as the circuits activity, which they may have been exposed to previously.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.100
GPT teacher head0.297
Teacher spread0.197 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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