Cloud meets classroom: experience report on using IBM Bluemix in a software architectures course
Why this work is in the frame
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Bibliographic record
Abstract
The process of teaching software architectures should go beyond abstract concepts (such as quality attributes, architectural tactics, patterns, and methods) to getting students to recognise and implement them practically. Clearly, for this, project work is essential so as to familiarise students with the key technologies and tools. We note that technology, widely popular in industry for hosting business services, is quite suited to teaching about service-oriented architectures and micro-services. However, our analysis suggests that the use of cloud technology in software architecture (SA) courses is not very strong in tertiary institutions. Given the time constraints in SA courses, the learning curve on both administrative and technical aspects of the underlying infrastructure should arguably be minimised so as to enable focus on the core features of the course. In this paper, we share our experience on using IBM Bluemix in a half-term course on software architectures at the University of Western Ontario. In particular, we note that while students need to familiarise themselves with the technology and the opportunity it provides for supporting end-user services, the learning curve of Bluemix is gradual enough for students to accomplish creating plausible services in a real world environment. This paper describes a number of observations and lessons learnt from the points of view of both students and instructors.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it