MétaCan
Menu
Back to cohort
Record W3001460549 · doi:10.24908/pceea.vi0.13857

ASSESSMENT FOR LEARNING: USING AGILE PROJECT MANAGEMENT METHODS IN SECOND-YEAR ELECTRICAL ENGINEERING DESIGN PROJECT

2019· article· en· W3001460549 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFormative assessmentAgile software developmentProject managementComputer scienceProduct (mathematics)Project-based learningEngineering managementEngineeringKnowledge managementSystems engineeringSoftware engineeringMathematics educationPsychology

Abstract

fetched live from OpenAlex

Assessment for learning and formative assessment practices have been shown to provide students with improved learning outcomes. Specifically, assessment for learning provides students with feedback that can be used to improve their future performance. This feedback loop is similar to the processes used in agile project management, where short iterations between product demos provide quick feedback to align the product with customer expectations. This paper will provide a case study of assessment for learning and agile project management being applied in second-year electrical engineering courses. The results show that students appreciated the learning opportunity that came with these activities.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.034
GPT teacher head0.321
Teacher spread0.288 · 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