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Record W1939552172 · doi:10.24908/pceea.v0i0.5737

The Use of an Open-Ended Project to Improve the Student Experience in First Year Programming

2015· article· en· W1939552172 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) · 2015
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProject-based learningVariety (cybernetics)Scope (computer science)SyntaxComputer scienceMathematics educationMechatronicsEngineering managementPsychologyEngineeringProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

Prior to 2010, Mechanical and Mechatronics Engineering students at the University of Waterloo were taught an introductory programming course using C++ in first year. Historically, the emphasis was on learning syntax; practising problem-solving was a distant second priority. In addition, many students were noticeably disengaged in lectures, and the assessments used were not authentic.Starting in 2010, a course project was implemented to address these concerns. The project was immediately well received by students, as evidenced by a noticeable number of students going well beyond the minimum project requirements and the variety of projects implemented. Since the project was introduced, the students have been able to successfully answer less structured final exam questions. The increase in problem-solving and thinking skills more than offsets the reduction in language-specific facts. The logistics, challenges and resources required to implement a project of this scope will be described

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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