Case Studies for Enhancing Student Engagement and Active Learning in Software V&V Education
Notice bibliographique
Résumé
Two critical problems facing the software (S/W) industry today are the lack of appreciation of the full benefits that can be derived from Software Verification and Validation (V&V) and an associated problem of shortage of adequately trained V&V practitioners. To address this situation, the software V&V course curriculum at the author’s institution is being improved via a National Science Foundation (NSF)-funded project. The basic objectives of this project are to enhance the quality of software education via increased student engagement and by bridging the gap between the basic principles discussed in the classroom and the complexity of real world problems. The teaching method utilized promotes higher levels of student engagement and learning through interactive, hands-on exercises, case studies and discussions. In addition, the instructional materials were purposefully designed not only for university classroom settings, but to also be deployed for on-the-job professional training in S/W industry settings, thereby helping to increase the pool of professionals with contemporary V&V knowledge and skills. The new course curriculum enhancement described in this paper is guided by academic research and industry best practices that focus on four specific V&V focus areas: requirements engineering, reviews, configuration management, and testing. Among many educational tools that are being developed to achieve the project objectives, the work related specifically to the development of one central component, case studies, is described here. Historically, case studies have been educational tools utilized in business, law, and medicine, but are not as prominent in software engineering. The hypothesis is that case studies would be effective educational tools to introduce real-world professional practices into the classroom, which would help the students in both identifying and solving problems, and developing a perspective on applying knowledge. In this paper we describe a set of V&V related case-studies that we have drawn from industry experiences and developed as pedagogical tools. These case-studies cover several important topics in the S/W V&V domain such as software testing, legal issues in software, software consumer protection, and requirements from the customers’ perspectives. We also report on the results of initial implementation of the case studies related to software testing in the classroom and show their effectiveness both in terms of satisfying Accreditation Board for Engineering and Technology (ABET) outcomes assessment and student satisfaction.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».