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Record W4282973461 · doi:10.31812/educdim.4519

Formation of software design skills among software engineering students

2022· article· en· W4282973461 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEducational Dimension · 2022
Typearticle
Languageen
FieldComputer Science
TopicInnovative Educational Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)Software engineeringSoftware developmentSocial software engineeringSoftware requirementsComputer scienceSoftware constructionPersonal software processSoftware designEngineering managementSoftware Engineering Process GroupSoftwareEngineeringPsychology

Abstract

fetched live from OpenAlex

The study focuses on one of the mobile-oriented environment competence components for software engineering (SE) students. It has been demonstrated that the implementation of the higher education standard for SE bachelors has generated a number of issues in terms of ensuring training quality, principally due to a lack of specification for both skills and learning outcomes. Designing a precise framework of professional competencies for SE bachelors is one method to overcome these issues. The research examines methods for developing K14 (the ability to participate in software design, including modeling (formal description) of its structure, behavior, and working processes), a critical particular professional competency for future software engineers. Recommendations for software design teaching techniques, learning content, modeling and design tools, and assessment of the level of formation of relevant competence are developed based on a historical and genetic review of software design training among SE students in the UK, USA, Canada, Australia, New Zealand, and Singapore. The industrial-style software design training (studio training) is used as an example. The transition from architectural to detailed design, as well as project implementation, are discussed. The study's future prospects include substantiating the third engineering component of SE – software construction (after requirements engineering and design engineering).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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