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Record W3180394220 · doi:10.24908/pceea.vi0.14881

FIRST YEAR COMMUNICATIONS CLASSES: APPLICATIONS OF CRITICAL EVALUATION OF INFORMATION IN A PROBLEM-BASED LEARNING FRAMEWORK

2021· article· en· W3180394220 on OpenAlexaffvenue
Kate Mercer, Kari D. Weaver, George Lamont, Christine Moffatt

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVariety (cybernetics)Context (archaeology)Multidisciplinary approachScope (computer science)Knowledge managementSustainabilityComputer sciencePsychological interventionInformation seekingEngineering ethicsPsychologyEngineeringSociologySocial scienceLibrary science

Abstract

fetched live from OpenAlex

Increasingly in an information-centric society, educational institutions must navigate ideological and pragmatic approaches to teaching how and where students find information used to make decisions. Engineering students’ information-seeking needs must also navigate a variety of competing sources of information—their professors, the library, their peers, family and friends, and industry professionals. Undergraduate engineering students are faced with learning both fundamental engineering concepts and soft skills such as information seeking and communication. One approach to teaching information seeking and communication could beProblem-Based Learning (PBL), which is a teaching method focused on having groups use open-ended realworld problems as a context for learning new concepts. This paper will provide a summary of the current scope of literature around PBL, implications for sustainability, andcontextualize it within the multidisciplinary context of library-focused interventions in first year communications courses.

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)0.002
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 score0.223
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.008
GPT teacher head0.240
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

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
Published2021
Admission routes2
Has abstractyes

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