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Record W2886631956 · doi:10.24908/pceea.v0i0.9474

STUDENT FEEDBACK AND PROBLEM DEVELOPMENT FOR WEBWORK IN A SECOND-YEAR MECHANICAL ENGINEERING PROGRAM

2018· article· en· W2886631956 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) · 2018
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBlackboard (design pattern)UsabilityComputer sciencePreferenceEngineering educationMathematics educationProblem-based learningOpen sourceSoftware engineeringMultimediaHuman–computer interactionEngineering managementEngineeringMathematicsProgramming languageSoftware

Abstract

fetched live from OpenAlex

Abstract – WeBWorK is a widely-used open-source,
 online homework tool where instructors may author their
 own problems, or select problems from an Open Problem
 Library. While it is extensively and globally used in
 mathematics, there are few problems available for
 engineering subjects. Due to initial student feedback
 based on mathematics problems, we decided to compare
 WeBWorK directly to our Blackboard Learn LMS for
 online homework during an integrated second-year
 Mechanical Engineering program.
 Students were assigned two problem sets in
 Blackboard and two problem sets in WeBWorK, and then
 completed a survey. Results show a strong preference for
 WeBWorK in all areas, including ease of use, ease of
 navigation, clear feedback, reported enhancement of
 learning, etc.
 We outline the primary benefits and drawbacks of
 using WeBWorK, and conclude by recommending
 WeBWorK for online homework in engineering courses.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
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
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.218
Teacher spread0.213 · 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