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Record W2909805403 · doi:10.18260/1-2--29424

Learning Experience in Mechanical Engineering First-Year Students

2024· article· en· W2909805403 on OpenAlexaboutno aff
Hamid Farhadi Rad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEngineering educationHuman–computer interactionMathematics educationEngineeringEngineering managementPsychology

Abstract

fetched live from OpenAlex

Retention of engineering students continues to be a major challenge affecting engineering Schools across the nation and unsuccessful experiences in freshmen engineering and science courses are some of the driving factors contributing to this problem.This paper presents details of a reformed freshmen course offered in the mechanical engineering (ME) program at Washington State University-Vancouver (WSU-V).It is a semester long two-credit course with a primary purpose of giving the students an opportunity to explore the mechanical engineering discipline topics that they are going to learn in their four-year study.This course has been offered for the past ten years with various teaching approaches.It is mostly a project-based course combined with lectures across the mechanical engineering topics, such as force/stress analysis, material properties, motion, fluids, etc.In the first few offerings, ME faculty members were invited as guest speakers to present their areas of research to the students.Based on their availability, mechanical engineers from local industry were invited as guest speakers to talk to the students about "a day in an engineer's life."These approaches have had various outcomes and instructors have varied methods to meet the needs of students.In the new approach, in addition to covering the engineering fundamentals and problem solving, the students are engaged in two group projects enhancing their creativity and hands-on skills.One is a term project, similar to the ones assigned in previous years.The additional project proposed at the freshman level was on reverse engineering.The paper provides details of how the course was organized, topics presented in the course, and the types of projects assigned to the students.Results on the student learning outcomes and experience throughout the course conclude the paper.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.447

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)0.0000.000

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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