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Record W2401624752 · doi:10.1109/cseet.2016.27

Teaching Agile Collaboration Skills in the Classroom

2016· article· en· W2401624752 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
TopicSoftware Engineering Techniques and Practices
Canadian institutionsCarleton University
Fundersnot available
KeywordsAgile software developmentScrumExtreme programming practicesAgile usability engineeringExtreme programmingEngineering managementAgile Unified ProcessComputer scienceSoftware engineeringSoftwareSoftware developmentCurriculumLean software developmentThe InternetEngineeringKnowledge managementSoftware development processWorld Wide WebPedagogySociology

Abstract

fetched live from OpenAlex

Agile methodologies like Scrum or Extreme Programming have come a long way over the last fifteen years. Recent quantitative studies show that many companies have successfully adopted agile methodologies. It was found that in agile software development, experience leads to collaboration. It could also be shown that successful professional agile teams tend to use more collaboration practices. In 2013, the new Computer Science studies at the University of Applied Sciences were started. For this, a new curriculum was developed. This paper presents and discusses the lectures, labs and educational software projects in the programming and software engineering modules. It is discussed how agile collaboration and collaboration practices can be taught in the classroom. For this, the setup and observations of an agile student project are presented and different online collaboration tools are discussed. It is argued that software engineering education benefits significantly from embracing the modern collaboration tools the Internet has made available.

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: none
Teacher disagreement score0.878
Threshold uncertainty score0.083

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 communication0.0000.001
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 head0.008
GPT teacher head0.266
Teacher spread0.258 · 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