MétaCan
Menu
Back to cohort
Record W2084934305 · doi:10.1109/fie.2011.6142882

Connecting reality with theory — An approach for creating integrative industry case studies in the software engineering curriculum

2011· article· en· W2084934305 on OpenAlexfundno aff
Joe Bolinger, Michael Herold, Rajiv Ramnath, Jayashree Ramanathan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaNational Science Foundation
KeywordsMirroringCurriculumVariety (cybernetics)Computer scienceDisciplineRelevance (law)Field (mathematics)AppealEngineering ethicsRealismEngineeringPedagogyPsychologyArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

Case studies have been successfully integrated into a wide variety of educational contexts and disciplines. Today, case studies are increasingly accepted as valuable teaching tools in science and engineering curriculums to complement the underlying theory of the field. Well-articulated cases can reinforce abstract concepts, demonstrate the nature of real client interactions, and showcase the relevance of soft skills to students that lack significant practical experience. However, assembling and delivering quality case studies to students requires a great deal of practical disciplinary knowledge, and a careful alignment of the case content and delivery style with curricular objectives, course learning outcomes, and the overarching institutional format. In this paper, we summarize our experience with an approach for constructing case study teaching materials that are integrative and deep in content, but also carefully aligned to the core principles and format of a senior-level software engineering course. Our approach ensures that the cases are complex enough to retain their realism and intrinsic appeal, while mirroring the format and objectives of the course such that the cases reinforce key points in a familiar and consistent fashion to the students.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.583
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
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 head0.118
GPT teacher head0.345
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreMethods

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

Citations10
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering Techniques and PracticesFrench-language works237,207