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Record W4378472744 · doi:10.3390/su15118598

Strengthening Sustainability in Agile Education: Using Client-Sponsored Projects to Cultivate Agile Talents

2023· article· en· W4378472744 on OpenAlexafffund
Linying Dong

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsToronto Metropolitan University
FundersRyerson University
KeywordsAgile software developmentScrumLean software developmentSustainabilityProcess managementEngineering managementKnowledge managementWork (physics)EngineeringExtreme programming practicesBusinessSoftware developmentComputer scienceSoftwareSoftware development processSoftware engineering

Abstract

fetched live from OpenAlex

The success of agile in software development (SD) has sparked the application of agile in non-SD domains such as business management to improve operational efficiency and innovation. Despite the rising industry demands for agile talents in the non-SD domains, agile education falls short of client-sponsored projects, calling into question the sustainability of agile education. This study makes up for the gap and illustrates an eight-month endeavor where scrum practices and values were imbued in a client-sponsored project. The analysis of qualitative and quantitative data gathered throughout the eight-month project illustrates a large disparity among students in their scrum application, reveals top challenges faced by students in their scrum application, and suggests the impact of the scrum application on the quality of student work. The findings of the study set a solid foundation based on which future agile education could be enhanced to strengthen the sustainability of agile education to meet industries’ rising demands for agile talents in non-SD domains.

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 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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.023
GPT teacher head0.343
Teacher spread0.320 · 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 teacher head, not a consensus.

Study designObservational
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

Citations3
Published2023
Admission routes2
Has abstractyes

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