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Record W2963477121 · doi:10.1145/3304221.3325560

Development of a Checklist Tool for Teaching Problem-Solving Skills

2019· article· en· W2963477121 on OpenAlex
Hillary Dawkins, G Altman Douglas, Kevin Glover-Netherton, David Hudec, Sean Lunt, Dalton Polhill, Mostafa Hamdy Rashed, Matthew Sampson, Alliyya Mohammed, James L. Mosley, Rhys Young, Judi McCuaig

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComputer scienceChecklistOverhead (engineering)Mathematics educationManagement scienceProgramming languageMathematicsPsychology

Abstract

fetched live from OpenAlex

Problem solving is a critical skill for computer science. However, the complexity and overhead of most programming tasks make problem solving difficult to explicitly teach. Previous studies describe explicit instruction of problem-solving strategies, but provide few details about how the strategies have been developed. We describe the development of a checklist tool that guides students in solving general programming tasks. The tool was developed by observing problem-solving strategies used by advanced computer science students in practice. We believe that the tool will provide a basis for explicit problem-solving instruction for novice programmers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.961
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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