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Learning Scrum

2020· book-chapter· en· W3088902501 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

VenueAdvances in systems analysis, software engineering, and high performance computing book series · 2020
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsScrumAgile software developmentModalitiesKey (lock)Computer scienceSoftware engineeringEngineeringSoftwareEngineering managementSoftware developmentSociology

Abstract

fetched live from OpenAlex

The LEGO®-Scrum simulation-based training (SBT) described here shows how LEGO® bricks can help professionals learn first-hand about Scrum methodology, an Agile approach to software development projects. The chapter's objectives are 1) to present the modalities of the LEGO®-Scrum SBT, 2) to demonstrate how LEGO® bricks can help professionals learn, first-hand, about Scrum, and 3) to illustrate how this learning can be relevant and impactful for participants. Based on observations, interviews, and a data collection by questionnaire carried out with 198 participants, the proposed SBT appears to provide a significant, relevant, and valuable learning experience. In addition, four experienced Scrum masters and IT project managers, who played key roles in the SBT, argued that the LEGO®-Scrum SBT provides a realistic representation of real-world Scrum projects; that it is dynamic, complex, challenging, and motivating; and that participants' learning is evocative and relevant, since they learn by doing.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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