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Record W2791863188 · doi:10.1145/3159450.3159530

Tracing vs. Writing Code

2018· article· en· W2791863188 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsTracingCode (set theory)Computer scienceTRACE (psycholinguistics)Ray tracing (physics)Mathematics educationProgramming languagePsychologyLinguisticsPhysicsOptics

Abstract

fetched live from OpenAlex

Much work has been done on the achievement gap between code tracing and code writing in CS1 students. The generally accepted explanation for this gap is that tracing and writing form separate steps in a learning scaffolding; students must first learn to trace code before they can be expected to write code. The expectation is that once students have mastered these skills, future grades will be driven by their ability to understand the deeper learning concepts, and so the gap between tracing and writing should disappear. In this paper, we detail and evaluate a study on 384 CS2 students to evaluate whether a tracing-writing gap still exists, and assess whether anything can be deduced about students who continue to exhibit such a gap. We find that not only does the gap seem to have closed by CS2, students are equally likely to show a reverse gap in the writing-tracing direction. However, further analysis shows a strong correlation between students who do continue to have a gap (in either direction) and poor overall performance in the course.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.229

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 head0.022
GPT teacher head0.281
Teacher spread0.260 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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