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Record W3271803

Alternatives to Heavily-Weighted Final Exams in Engineering Courses

2013· article· en· W3271803 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

VenueTeaching Innovation Projects · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSummative assessmentClass (philosophy)Engineering educationPerspective (graphical)Mathematics educationComputer scienceComponent (thermodynamics)Engineering managementEngineeringPsychologyFormative assessmentArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In many engineering courses, cumulative final exams typically comprise the largest component of a student’s grade. However, from a learner’s perspective, final exams are frequently associated with high levels of stress, which may hinder student performance during the exam period. In this interactive engineering-focused workshop, I will discuss the challenges associated with final exams that are heavily- weighted and propose alternative forms of summative assessment of student learning. Through a demonstration of a survey conducted in a Civil Engineering class at the University of Waterloo, I will demonstrate students’ perspectives on final exams. Furthermore, using data from twenty-one engineering course outlines from the Massachusetts Institute of Technology (MIT), I will demonstrate common assessment approaches used in engineering courses. The workshop will conclude by asking participants to share ideas for alternatives to heavily-weighted final exams and will introduce some alternative methods of assessment that have been suggested in education literature.

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)0.000
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: Empirical
Teacher disagreement score0.365
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.021
GPT teacher head0.264
Teacher spread0.242 · 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