MétaCan
Menu
Back to cohort
Record W4242119943 · doi:10.1145/637610.544457

On using the web as a collaboration space in the context of an industrial simulation

2002· article· en· W4242119943 on OpenAlexaff
Sylvie Ratté, Jocelyne Caron

Bibliographic record

VenueACM SIGCSE Bulletin · 2002
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie Supérieure
Fundersnot available
KeywordsContext (archaeology)Computer scienceClass (philosophy)Factory (object-oriented programming)Space (punctuation)Problem-based learningMultimediaMathematics educationArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

We present a teaching method aimed at developing higher programming skills from description to production. The model is derived from problem-based learning approaches. It is supported by an "incremental" web site that gradually introduces theoretical presentations, examples, programs and information regarding the problem. The web site is also used as a collaboration space where students can find partial solutions proposed by other teams as well as "requests" submitted by a fictitious client. At the end of the project, each product is published and the best teams are awarded a virtual medal.We had four objectives. The first was to get students to share their expertise and learn to work in teams; second, to teach students the importance of doing a conceptual analysis rather than jumping into programming; third, to introduce theoretical notions, exercises, and examples in class when the students asked for them; and finally, to get students to formulate and describe problems by themselves.Students had to produce a large-scale project that consisted of simulating a factory. The project can be understood at two levels: the first is the problem of developing a discrete simulation of a factory; the second is the creation of the program itself which simulates the industrial context by requiring constant adjustment to new instructions and data.Although this approach requires a lot of effort and coordination on the part of the instructor, the benefits are definitely worthwhile. The model provides students with a broad, in-depth and rewarding learning experience.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.060
GPT teacher head0.309
Teacher spread0.249 · 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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueACM SIGCSE BulletinSame topicOpen Education and E-LearningFrench-language works237,207