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Record W2204643176 · doi:10.18260/1-2--21682

Measuring Undergraduate Student Perceptions of the Impact of Project Lead The Way

2020· article· en· W2204643176 on OpenAlexaboutno aff
Noah Salzman, Eric L. Mann, Matthew Ohland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsEngineering educationLikert scalePsychologyMathematics educationMedical educationQuarter (Canadian coin)EngineeringEngineering managementMedicineGeography

Abstract

fetched live from OpenAlex

A survey was distributed to the entire undergraduate student body at a large public university on students’ experiences in Project Lead The Way, a popular middle school and high school technology and engineering program. The survey included demographic questions including academic major, questions on which PLTW classes the students took in high school, and Likert-type ratings of those experiences.Of the responses to the survey (n=575), slightly fewer than half (n=252) indicated that they had participated in PLTW classes in high school. Approximately half of the respondents were majoring in engineering, one quarter in engineering technology, and the rest were distributed among the other colleges of the university. The most popular engineering majors indicated were mechanical engineering, electrical and computer engineering, civil engineering, and aeronautics and astronautics engineering. The most popular engineering technology majors were mechanical engineering technology,electrical and computer engineering technology, and computer graphics technology. 89%of the respondents were Caucasian, and 75% were male.Respondents were generally positive about the program, indicating that they looked forward to the classes, felt that the classes gave them a better appreciation of engineering and technology, and that the classes influenced their choice of major. Differences between the responses of engineering versus technology majors, those majors combined versus all other majors, and male respondents versus female respondents were generally small and not statistically significant.The survey also included an open response portion, where participants were asked if there was anything else they wanted to share about their PLTW experience. Many participants indicated that their experience helped them in choosing a college major and preparing them for college and helped them to learn and think like an engineer. Many participants also described their PLTW experience as “fun,” but although such comments are clearly positive, they do not advance our understanding of PLTW, because students have different ideas about what makes an activity fun. Further, if PLTW were “fun” at the expense of achieving important learning objectives, it would be a disservice. Participants were frustrated by the lack of college credit for their PLTW courses, poor teaching, and feeling like they were better prepared for technology coursework than engineering.The popularity of Project Lead the Way and the resources committed to the program nationally make it urgent that we develop a greater understanding of who this program is reaching and what outcomes result from participation. Further, it will be important to explore the mechanisms by which PLTW achieves those outcomes, which will help guide other pre-college engineering programs. Further research in the area is planned, and will benefit from the findings of this earlier survey.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.040
GPT teacher head0.274
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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