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Record W2886737464 · doi:10.24908/pceea.v0i0.10260

THREE QUARTERS OF A LOOP: CO-OP WORK TERM EVALUATIONS AND WORK REPORTS TO MEASURE THE COMMUNICATION ATTRIBUTE

2018· article· en· W2886737464 on OpenAlexafffundvenue
Andrew J. Milne, Mehrdad Pirnia, Rania Al-Hammoud, Jason Grove

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsTeamworkInterpersonal communicationWork (physics)Term (time)Leverage (statistics)Communication skillsMeaning (existential)PsychologyComputer scienceDiversity (politics)Process (computing)Medical educationKnowledge managementSocial psychologyEngineeringSociologyArtificial intelligenceManagement

Abstract

fetched live from OpenAlex

Abstract –This research analyzes the available data (student work term evaluations performed by employers, and work reports evaluated by the program) to triangulate and understand student performance on the CEAB Communication attribute. In this way we aim to innovate in the field of co-operative education to leverage the diverse work experiences of our students and understand their diverse backgrounds to suggest means of improving their communication skills. In this paper, we analyze employer feedback by grouping responses in several ways (binning by engineering program, term of study, level of performance, and criteria) to assess student performances at the faculty and program levels. We also assess student work reports for communication and analytical skills. We find a notable contrast between the evaluations given for the performance criteria. Students appear to perform at a higher level in areas such as Interpersonal Communication, Teamwork, and Appreciation of Diversity, while evaluations for criteria such as Problem Solving and Oral and Written Communication are relatively lower. This is supported by work report assessments showing that some students continue to struggle with written communication and analysis. Future work will include focus groups with employers and students to add meaning to the data. Throughout the process, writing and communication experts are involved to help interpret data and recommend solutions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.618

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 science0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 designObservational
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

Citations2
Published2018
Admission routes3
Has abstractyes

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