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Record W2886059665 · doi:10.24908/pceea.v0i0.10194

A MODEL TO DEVELOP PEER FEEDBACK SKILLS IN FIRST-YEAR ENGINEERING STUDENTS

2018· article· en· W2886059665 on OpenAlexafffundvenue
Stephen Mattucci, Jim Sibley, Jonathan Nakane, Peter Ostafichuk

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsCommunication sourceActive listeningComputer sciencePeer feedbackFace (sociological concept)CraftFace-to-faceHuman–computer interactionMultimediaPsychologyMathematics educationCommunicationTelecommunications

Abstract

fetched live from OpenAlex

Abstract – Giving and receiving feedback is a necessary, but often difficult skill for young engineers to acquire. We developed and piloted the delivery of a feedback model as part of the first-year engineering experience at the University of British Columbia. The approach is based on recognizing feedback as a form of professional communication, and that it requires practice to improve. We wove different aspects of communication skill development through two large newly-designed first-year introduction to engineering courses, building towards face-to-face feedback through a staged series of communication experiences. 
 The full feedback model highlighted the nuances of face-to-face communication, and was called the "3×3", since it includes the three components involved in face-to-face feedback (sender, message, and receiver), each with three associated aspects. The sender uses appropriate words and body language, ensures proper interpretation, and is empathetic; the message is objective and non-judgmental, sufficiently detailed, and contains suggestions for improvement; and the receiver remains open and listening, acknowledges to the sender that they are listening, and clarifies to ensure understanding. 
 Students applied what they had learned through an activity reviewing poster presentations from a major course design project. In the activity, they each had an opportunity to craft a feedback message before delivering the message face-to-face to a peer. Students then reflected on the feedback they received by summarizing the message, recognizing how the sender delivered the feedback, and identifying why the feedback was helpful. Student reflections were analyzed for themes from the 3×3 model. 
 Students found feedback from peers particularly helpful when it was delivered in an appropriate and courteous manner, checked for proper interpretation, provided clear suggestions for improvement, and was coupled with praise of something that was done well.
 Providing students with a structured model allows them to follow a process in both providing effective face-to-face feedback, but also better appreciate why receiving feedback is beneficial in helping them improve.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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