MétaCan
Menu
Back to cohort
Record W2057222830 · doi:10.1145/1099203.1099229

Collaboration support for novice team programming

2005· article· en· W2057222830 on OpenAlexaff
Davor Čubranić, Margaret Anne Storey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceUsabilityPair programmingSoftware engineeringCode (set theory)Human–computer interactionExtreme programmingCollaborative softwareVisual programming languageDevelopment environmentMultimediaKnowledge managementProgramming languageSoftware developmentSoftwareSoftware development process

Abstract

fetched live from OpenAlex

Learning computer programming in a modern university course is rarely an individual activity; however, IDEs used in introductory programming classes do not support collaboration at a level appropriate for novices. The goal of our research is to make it easier for first-year students to experience working in a team in their programming assignments. Based on our previous work developing and evaluating IDEs for novice programmers, we have identified two main areas of required functionality: 1) features for code sharing and coordination; and 2) features to support communication. We have extended an existing teaching-oriented integrated development environment (called Gild) with features to support code sharing and coordination. We report on a preliminary study in which pairs of students used a prototype of our collaborative IDE to work on a programming assignment. The goals of this study were to evaluate the effectiveness and usability of the new features and to determine requirements for future communication support.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicTeaching and Learning ProgrammingFrench-language works237,207