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Record W2082253354 · doi:10.1145/2597959.2597967

Industry in the Classroom

2014· article· en· W2082253354 on OpenAlex
Christopher K. Hobbs, Herbert H. Tsang

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsTrinity Western University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClass (philosophy)Experiential learningPerspective (graphical)Soft skillsSoftwareNeglectComputer scienceEngineering managementSoftware engineeringSoftware Engineering Process GroupSocial software engineeringSoftware developmentEngineering educationEngineeringKnowledge managementSoftware development processMathematics educationSoftware constructionManagementArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

This paper reports a software engineering class focused around experiential learning through an industry-partnered project. It includes a student's perspective on the class experience. The authors argue that software engineering classes that only utilize trivial homework neglect crucial software development soft skills and fail to prepare students for industry employment. By focusing the courses around and industry-partnered project, students were able to integrate the fundamental concepts of software engineering while being equipped with real-world experience. The authors believe the proposed approach allows students to be better equipped for the industry and provides them valuable experience in their future career.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.173

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Quick stats

Citations13
Published2014
Admission routes2
Has abstractyes

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