A Guideline of Using Case Method in Software Engineering Courses
Bibliographic record
Abstract
Software Engineering (SE) education has been reported to fall short in producing high quality software engineers. In seeking alternative solutions, Case Method (CM) is regarded as having potential to solve the issue. CM is a teaching and learning (T&L) method that has been found to be effective in Social Science education. In principle, instructors should be guided appropriately in order to adopt CM in T&L. SE education however lacks of such guidelines. This paper addresses this concern by identifying the factors and their corresponding elements and conditions that contribute to the effective use of CM in T&L SE courses. The factors, elements and conditions were then collated as a framework in the form of a guideline. The factors, elements and conditions were gathered through a series of studies, namely a theoretical study, two surveys and two expert reviews. The theoretical study involved reviewing previous research, while the surveys were performed with five groups of students who experienced CM in learning SE courses. The students were from various education and work backgrounds. Two types of survey instruments were employed, which are questionnaire and group interviews. To form the guideline, the gathered data were analysed qualitatively using contents analysis. The guideline was then validated by two experts through expert reviews. There are four main factors that constitute the guideline of using CM in T&L SE courses: Case, Instructor, Student and Infrastructure. Each factor has its corresponding elements and conditions. The guideline is useful for SE instructors to adopt CM in T&L SE courses at their institutions.
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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 | 0.086 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".