A Re-look at the Introduction to Software Engineering Course
Bibliographic record
Abstract
Abstract The Introduction to Software Engineering course is a fundamental course in not just software engineering programs, but also in computer science, computer engineering, and other computing programs. In many respects, the design of this course is more important for the other computing programs that require it than for the software engineering program because this is often the only exposure to software engineering principles that the non-software engineering students get. To address perennial student complaints about the course, and concerns raised by our Industrial Advisory Board, the faculty decided that we should take a relook at our Introduction to Software Engineering course. This course is the one in our curriculum that we have changed the most often, at least 6 times in the 20 year history of our program. We were continually balancing multiple requirements for the course including it needing to be an introduction to the breadth of software engineering, and a significant team project experience for the students. In reviewing the course's history, we decided that the reason this course was changed so frequently is that with each redesign we always started with the same basic premises for the course, namely, it needed to provide a broad overview of the software engineering discipline, and it would use one of the classic software engineering textbooks that covers all of those areas. The relook dropped both of those requirements. This paper describes the approach we used for developing this new version of our Introduction to Software Engineering course and the topics that are covered. Using an engineering approach to design the course, we set requirements for the topics to be distributed as 35% design, 35% process, 15% teamwork, and 15% communications. We describe the types of web-based resource material the course uses in place of a required textbook. The paper describes the requirements we placed on our web-based project and the particular project in use. The course ran in two pilot sections in spring 2017, and rolled out to the full offering of the course to approximately 250 students in fall 2017. Goals for the relook were to reduce the student complaints about the course, which we felt were valid complaints, while introducing the students to the most important concepts in software engineering, and to contemporary software development practices and tools. We will present our assessment of our achievement of these goals which resulted in receiving none of the prior complaints from students, and receiving thanks for how well the course material prepared students for the job interviews that they went on.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".