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Record W2126657415 · doi:10.24908/pceea.v0i0.4846

SMALL-GROUP TUTORIALS AND OPEN BRAINSTORMING FOR PROBLEM-SOLVING IN ENVIRONMENTAL ENGINEERING SYSTEMS

2013· article· en· W2126657415 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) · 2013
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBrainstormingClass (philosophy)CoachingEngineering educationComputer scienceGroup workResource (disambiguation)Work (physics)Mathematics educationProblem-based learningEngineering managementEngineeringPsychologyArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Environmental Engineering Systems is a core-course for undergraduate students at the University of Guelph pursuing degrees in Environmental or Water Resource Engineering. The class is a thorough introduction to many elementary concepts of Chemical and Biological Engineering, including concepts of conservation and reactor system design, and is delivered through lecture, laboratory and tutorial components. At the University of Guelph, Environmental Engineering Systems tutorials are operatedin small groups (approx. 20 students), taking advantage of the extensive whiteboards available in the facility to promote open brainstorming as a means to solve problems. Students work in partnerships, but are encouraged to discuss with the other groups in the room, as to come to a consensus solution on a given problem. Instructors (usually 2) float around the room, coaching students as needed, but refrain from providing over-guidance and a final solution; ensuring students be cognizant of problem solving in industrial and/or higher-learning settings. This technique was beneficial to instructors, allowing for the easy identification of specific problem-solving skills students were lacking, and the appropriate corrections to be implemented. There were still some concerns about engaging timid students, and also with students becoming over-dependent on the group dynamic and not performing as well individually.

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)0.001
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 score0.596
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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