Developing Industrial Cases for Teaching Software Engineering – A Lesson Learned
Bibliographic record
Abstract
Software engineers are provided with an enormous choice of technology for improving the quality of software. Being intangible, software products tend to be more intricate to build than any other artifacts. The selection of technology can thus become a critical factor for the success of software development. Software engineers are expected to be well-versed in various technologies to enable them to decide the best one for a particular development project. Sensible decisions however require not only understanding but also active minds, which can be achieved through meaningful learning. Being a discussion-based learning approach that encourages students to exploit knowledge and understanding of the subject matter, the Case Method seems to be a practical teaching and learning option. This method entails developing specific cases that promote exploration and critical thinking. To ensure the developed cases are useful, they should be evaluated. This paper presents a practical methodology for developing as well as evaluating industrial cases for teaching software engineering through the Case Method. It also shares some important lessons learned from the process. These lessons act as a guideline for future case developers to compose useful cases and motivate software engineering instructors to use cases in teaching.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".