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Record W4313331824 · doi:10.1145/3571785.3574128

Motivation and Strategies for Effective Inclusion of Cloud Solution Provider Certifications in Computing Curricula

2022· article· en· W4313331824 on OpenAlexaff
James H. Paterson, Joshua Adams, Derek Foster, Ouldooz Baghban Karimi, Zain Kazmi, Ruth Lennon, Sajid Nazir, Lee Stott, Laurie White

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInformation Systems Education and Curriculum Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCloud computingCertificationCurriculumInclusion (mineral)Computer scienceKnowledge managementPsychologyPedagogyManagement

Abstract

fetched live from OpenAlex

A series of Working Groups has met at previous ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas and those areas to student learning objectives. The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work.

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)0.014
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0090.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.270
Teacher spread0.251 · 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 designQualitative
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

Citations7
Published2022
Admission routes1
Has abstractyes

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