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Record W4212814613 · doi:10.1145/3478431.3499335

Exploring Common Writing Issues in Upper-Year Computer Science

2022· article· en· W4212814613 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.

Bibliographic record

VenueProceedings of the 53rd ACM Technical Symposium on Computer Science Education · 2022
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRubricFormalityCLARITYComputer scienceCurriculumGrammarMathematics educationTechnical writingLinguisticsPedagogyHigher educationPsychology

Abstract

fetched live from OpenAlex

This study analyzes common issues in the writing of our upper-year, undergraduate computer science students in timed (e.g. tests) and untimed (e.g. longer assignment reports) scenarios. Our goal is to identify writing issues that should be addressed earlier in the CS curriculum. In collaboration with a writing specialist, we develop and fine-tune a rubric with Grammar, Conciseness, Clarity, Organization, Structure, and Formality as the main categories.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0100.007
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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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