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Record W2886970569 · doi:10.24908/pceea.v0i0.10263

SHIFTING RESPONSIBILITIES: USING PEER ASSESSMENT IN SENIOR, ENGINEERING DESIGN TO PROVIDE EFFECTIVE SUPPORT AND MEANINGFUL FEEDBACK DESPITE LARGE CLASS SIZE

2018· article· en· W2886970569 on OpenAlexaffvenueabout
Andrea Bradford, Julie Vale

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRubricGrading (engineering)Peer feedbackPeer assessmentClass (philosophy)Computer sciencePeer reviewPeer learningTechnical peer reviewMathematics educationMedical educationPsychologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Abstract – Urban Water Systems Design is a required, senior engineering design course for undergraduate students in the Water Resources and Environmental Engineering programs at the University of Guelph. A central component to this course has been a challenging, stormwater management design and simulation project. Recently, enrollment has increased from approximately 50 to 90 students. With these increased numbers, it is no longer feasible for the instructor to provide individualized, rich and robust feedback on the project. Rather than eliminating or simplifying the project, which is a highly valuable learning activity, peer assessment was investigated as an option.
 In Fall 2016, peer assessment was implemented for two term tests and a design project. In addition to addressing resource constraints, this shift in responsibilities takes advantage of the active, collaborative, learning opportunities provided by grading tests in class and giving feedback to and receiving feedback from others on design work and report writing. To achieve the largest benefit possible, best practices suggested in the literature were followed, such as training peer assessors, including developing a rubric with the class to enhance understanding of expectations; using multiple assessors to address student concerns about fairness; and incorporating reflection on the peer assessment activities.
 Data were collected through two surveys administered before and after the course’s peer evaluation activities and through graded reflections on peer evaluation activities. Most students thought they learned as much or more than they would have without the incorporation of peer evaluation. Based on predominantly positive student comments and fair grading, peer grading of tests will be used in future with a few minor modifications. Most students also found the peer assessment of design reports to be fair, with a reasonable time commitment. Some students were troubled by the variability of grades given by peers. Enhanced training was suggested to help students grade more consistently and provide more effective feedback.

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)0.003
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 score0.210
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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