MétaCan
Menu
Back to cohort
Record W2601511489 · doi:10.24908/pceea.v0i0.6540

Coaching for Communicative Competence: A Student-Focused Approach

2017· article· en· W2601511489 on OpenAlexaffvenueabout
Aidan Topping

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCoachingCapstoneCommunicative competencePsychologyCompetence (human resources)CurriculumCommunication skillsPedagogyMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

This paper focuses on instructor led, student-focused coaching sessions undertaken in the senior (capstone) design classes at the University of Manitoba. The team-based design approach used in capstone courses allows students to work in a manner more closely reflecting industry practice; however, team writing does not allow for individualized scaffolding which could ensure each graduate meets the standard for communicative competence. Rather than allow students to rely on the team’s collective communication skills, we developed an approach that incorporates individual coaching sessions at multiple stages in the writing process. These sessions require students to reflect upon their work, and allow them to discuss it in a meaningful way with the instructor. Doing so at various stages affords students the opportunity to engage in an iterative approach to developing communicative competence: applying what they learn, reflecting on their work, and discussing communicative gains and new methodologies.While integrating individual coaching and directed instruction into the curriculum can be challenging, this paper demonstrates how student-focused coaching sessions provide a platform from which senior design students can increase both communicative competence and their value to industry as future engineers

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.565
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.028
GPT teacher head0.350
Teacher spread0.322 · 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.

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

Citations1
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicReflective Practices in EducationFrench-language works237,207