Success Factors for Using Case Method in Teaching and Learning Software Engineering
Bibliographic record
Abstract
The Case Method (CM) has long been used effectively in Social Science education. Its potential use in Applied Science such as Software Engineering (SE) however has yet to be further explored. SE is an engineering discipline that concerns the principles, methods and tools used throughout the software development lifecycle. In CM, subjects are presented to students by means of real cases whereby students themselves either individually or in group discussions work through the problems and issues presented in the cases. The CM approach is deemed necessary for SE education in order to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems. As a largely theoretical subject, SE students could understand more about the practical application of SE concepts and ideas via such active learning activities. This paper presents a survey conducted on two sets of students who were exposed to CM in learning SE. Besides confirming the acceptance of CM among SE students, the surveys aimed to discover the contributing factors and elements that influence the efficacy of the method. The participants consisted of 64 undergraduates that comprised local full-time and executive students. The survey was performed in two semesters through group interviews. Data from the survey were analysed qualitatively using content analysis. The results showed that there are four factors that are important to teaching SE using CM, namely Environment, Case, Instructor, and Student. Each of these factors has certain criteria and characteristics that suggest how CM can be successfully used in teaching and learning SE. These findings can be used by SE educators to more effectively plan the use of CM as one possible teaching method in SE.
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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 | 0.026 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".