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Record W4401313602 · doi:10.18260/1-2--47542

How We Teach: Chemical Engineering Electives

2024· article· en· W4401313602 on OpenAlexaboutno aff
Laura Ford, Janie Brennan, Heather Chenette, Jennifer Cole, Kevin Dahm, David Silverstein, Stephen W. Thiel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
FundersVanderbilt UniversityAmerican Society for Engineering Education
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

The AIChE Education Division's Survey Committee covered elective course offerings in chemical engineering departments in the US and Canada in the Fall 2023 survey.Results are available from 70 respondents at 69 institutions.Medians are presented here, and the mode is mentioned if it is different from the median.Institutions require that their students take two chemical engineering elective courses, one outside-of-department technical elective (mode of zero), and four total technical electives.Undergraduate-only institutions (N = 5) offer a median of 8 chemical engineering elective courses over a two-year period, with a range of 2 to 28 courses.Over the same time frame, departments with graduate programs offer a median of 3 elective courses to just undergraduate students (mode = 2), 4 elective courses to undergraduates that graduate students may take (mode = 0), and 6 graduate courses that undergraduates may take as electives (mode = 0).Elective class sizes are small, with 44% of institutions reporting a typical enrollment of under 15 undergraduate students per course and 50% reporting 15 -30 undergraduate students per course.In the past ten years, 17% of departments have converted a required course to an elective.These now-electives were most often required bioprocessing, advanced chemistry, or molecular engineering courses.More departments, 26%, reclassified technical electives as required courses in the past ten years.These now-required courses were usually process safety, programming, or statistics elective courses.Bio-, energy-, and materialstype electives were offered at over three-quarters of departments.Advanced-core and sustainability electives were the next most popular, at 60% of institutions.Process-type electives were offered in 47% of departments.In the past ten years, almost all departments created a new technical elective that has been regularly offered.Bio-type electives were the most common new elective, followed by process-type electives.Data analysis, data science, and process simulation & modeling were the only emerging topics to be covered in an elective course at half or more of the departments.Details about course titles, electives with laboratory components, minors & concentrations, and common out-of-department electives are provided in the proceedings.Comparisons were made to the results from previous surveys when possible.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score0.760

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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 designBench or experimental
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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