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Record W4391602944 · doi:10.18260/1-2--44007

Quarter to Semester Transition: Lessons Learned from a Mechanical Engineering Case

2024· article· en· W4391602944 on OpenAlexaboutno aff
Amanda C. Emberley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumProcess (computing)Work (physics)Engineering managementService (business)Computer sciencePerspective (graphical)Engineering educationQuarter (Canadian coin)Unit (ring theory)Engineering design processMedical educationEngineeringMathematics educationPedagogyPsychologyMechanical engineeringArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Abstract This is an evidence-based practice paper. Our university is in the process of a mandatory transition from a quarter based system to a semester based system. This involves completely transitioning our entire curriculum in a university wide effort, involving many different perspectives and opinions. In this paper, we aim to outline lessons learned from this challenging project, specifically from the perspective of the mechanical engineering program, but with applications to any major. We focus on how we approached the problem, the challenges and successes in working with other departments on our campus, and our methods of preserving the strengths of our current program, while also taking the opportunity to modernize our curriculum. Those strengths we aim to preserve include: a focus on hands-on, active learning, design experiences in each of the four years, and concentrations that allow students to develop and focus their particular interests. Struggles we have encountered include managing ever changing requirements, working across the college to standardize what we call service courses (courses that are taken by multiple majors), and managing the high unit load of our high number of lab courses. We have found success with a wide range of strategies, which we will describe in the paper. These have included a backwards design (starting with the outcomes for the students), having a small team focused on the details of the planning with frequent feedback from the department, and college wide meetings to compare work and share ideas.

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: none
Teacher disagreement score0.796
Threshold uncertainty score0.628

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 head0.022
GPT teacher head0.256
Teacher spread0.235 · 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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