Exploration in Facilitating Learning Experiences Towards Inspiring Responsible Software Engineers
Bibliographic record
Abstract
Johnson poses the question, “what does it mean to be a responsible engineer?” Characteristics could be wide-ranging. Engineers Canada helps by defining graduate attributes (GAs). All GAs are important. However, GA-9 “impact(s) of engineering on society and the environment” is one characteristic that this author proposes is fundamental. The idea of sustainable design and development has seen increasing conversation and engagement in our field in recent years. With initiatives such as the United Nations (UN) “decade of action (DoA),” engineers have the innate responsibility to help deliver the promise of positively transforming our world by 2030 and beyond. Exposing engineering learners to individual and collaborative knowledge-building experiences around the idea of sustainability, and what it means to be sustainable citizens may assist. It could be as we engineers become more knowledgeable in this realm, so too might everyday citizens in their interactions with our creations. Reflecting on Quan-Haase’s idea of technology as society, relating to the idea that society advancements are in large part intertwined with advancements in technology, software engineers may have a significant role to play. This role could include the engineering of community-based computer technologies that engage citizens in knowledge-creating activities towards the betterment and well-being of society. This work explores the following questions. Can inspiration towards becoming a responsible engineer be instilled in engineering learners in academia? Can this be accomplished by facilitating a learning experience that immerses engineering learners in researching and exploring the design and development of computer technologies in support of the UN Sustainable Development Goals (SDGs)? Through resulting explorations, might both learners and everyday citizens who interact with the engineered creations be better equipped to participate in the UNs DoA, and beyond? This paper will describe a software systems engineering course at the University of Regina that facilitated a learning experience around these questions. A discussion regarding the structure of the course, its educational content, and results and feedback obtained on the learner experience will be provided. As well, ideas for continued exploration of this work will be discussed.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".