Connecting reality with theory — An approach for creating integrative industry case studies in the software engineering curriculum
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".