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Record W2346790846 · doi:10.1145/2910925.2910942

Impact of Software Usage on Fundamental Engineering Courses

2016· article· en· W2346790846 on OpenAlex
Ehsan Ahmed, Musfiq Rahman

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsCurriculumComputer scienceSoftware engineeringSoftwareBridging (networking)Engineering educationEngineering managementEngineeringProgramming language

Abstract

fetched live from OpenAlex

In the last two decades, the use of computer and particularly the personal computers has brought revolution in the teaching of Engineering courses. Computer-aided design and sophisticated analysis packages have changed the engineering curriculum, making it possible for students to analyze and design at a level of precision impossible to accomplish with hand calculations alone. Much of this improvement, however, occurs at the upper-end of the engineering curriculum. At the introductory level, the impact of computer software on the teaching of fundamental concepts has been less successful. However, in USA and some other developed countries, the use of computer analysis package is also encouraged at the introductory level. This paper will critically examine the impact of computer software/analysis packages on the students' learning with reference to structural engineering course. It will also highlight the bridging gained and pitfalls observed from this recent changed in the engineering curriculum.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.570

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicExperimental Learning in EngineeringFrench-language works237,207