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Record W1818173261 · doi:10.24908/pceea.v0i0.4673

Creating A Separate Introductory Computer Science Course For Engineers: An Experience In Moving From Java To Python

2012· article· en· W1818173261 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPython (programming language)JavaComputer scienceCurriculumScience and engineeringCourse (navigation)Mathematics educationProgramming languageSoftware engineeringComputer Science and EngineeringEngineering ethicsEngineeringMathematicsPedagogySociology

Abstract

fetched live from OpenAlex

The University of Manitoba (U. of M.) 2011-12 curriculum has a new introductory programming/computer science course specifically for current or future engineering students. This course focuses on teaching fundamentals of programming and computer science through mathematical computation using Python. The need for this course came from a grass roots movement by engineering professors to evaluate the previous course and provide direction and/or assistance to the department of computer science. The committee felt it was time to abandon Java as the introductory language for engineers. This talk discussesthe resulting course; the challenges in selecting the version of Python to use; and the difficulties and rewards of the change to a new and previously untaught language at the U. of M.

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.

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)0.001
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 score0.480
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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