A Large Scale Agile Teaching Framework for Software Engineering
Date
2022-12-19
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Abstract
There has been a great deal of interest in software engineering as a rewarding career in recent years as industry demands for software professionals continues to rise. As such enrollments in tech-related majors such as software engineering and computer science continues to increase. There are several sources available for learning software engineering including Massive Online Open Courses (MOOCs). Meanwhile, universities are the primary providers of high-quality instruction in this field. Universities have to accept many students, which has created many challenges, such as reducing the quality of education and difficulty managing classes by instructors and assistants. Universities also need to increase their faculty members and improve the educational infrastructure. The industry is changing rapidly and demands graduates to adapt to the needs of the industry as quickly as possible. In addition, they are expected to have some soft skills, such as critical thinking and teamwork, that make university training harder. Various methods have been developed for software engineering education to manage the challenges of large enrollments and providing hands-on learning. These methods are based on active learning, which focuses on the learner rather than the educator, and require more work from instructors. This thesis provides a framework for teaching software engineering (SE) that utilizes DevOps concepts in teaching to respond to the needs of universities, based on agile methodologies and project-based learning that have matured in the industry and educational field after many years. We used machine learning and ML4Code methods to address the challenges of providing scalable feedback in universities, which is an essential need for a practical discipline such as software engineering. During the winter of 2021, this framework was implemented in ENSF 607 - Advanced Software Development and Architecture at the University of Calgary. It was evaluated based on the students’ perceptions of its impact on their learning journey.
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Keywords
Agile-Based Teaching, Machine Learning, Teaching Automation, EduOps, ML4Code
Citation
Bahrehvar, M. (2022). A Large Scale Agile Teaching Framework for Software Engineering (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.