Design, Development and Delivery of Active Learning Tools in Software Verification & Validation Education
Bibliographic record
Abstract
Active learning tools are critical in imparting real world experiences to the students within a classroom environment. This is important because graduates are expected to develop software that meets rigorous quality standards in functional and application domains with little to no training. However, there is a well-recognized need for the availability of effective active tools. This need has been addressed by the authors by designing, developing, and delivering, twenty delivery hours of Case Studies, sixteen delivery hours of Class Exercises, and six delivery hours of Video Case Studies for use in V&V courses. The active learning tools focus on some specific SV&V topics such as requirements engineering, software reviews, configuration management, and software testing. Four key skill areas sought after by employers, namely communication skills, applied knowledge of methods, applied knowledge of tools, and research exposure have been used to drive the development funded by a National Science Foundation grant and perfected through an industry-academia partnership. These tools have been successfully disseminated to over 25 universities with many CS, IS, SE programs incorporating the tools in their existing courses and others designing new courses based on these tools.In this paper we present data on the student feedback and pedagogical effectiveness of the strategies used to effectively incorporate and deliver the developed active learning tools by instructors at two universities. Traditional and flipped classroom delivery strategies are discussed as well as topics like pre-requisite knowledge preparation prior to class, course module presentation sequence, homework, team/individual work, collaborative discussions, and assessment tools are deliberated. The student questionnaire data from the two University Partners who used the V&V instructional activities were quite positive and showed that students were interested in the activities, saw the real-world applications, and communicated with their classmates as they solved the problems. Educational outcomes assessment demonstrated more effective learning in all key learning areas.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".