A Large Scale Agile Teaching Framework for Software Engineering

Bahrehvar, Majid

A Large Scale Agile Teaching Framework for Software Engineering

dc.contributor.advisor	Moshirpour, Mohammad
dc.contributor.author	Bahrehvar, Majid
dc.contributor.committeemember	Far, Behrouz
dc.contributor.committeemember	Johnston, Kimberly
dc.date	2023-02
dc.date.accessioned	2023-01-03T16:31:41Z
dc.date.available	2023-01-03T16:31:41Z
dc.date.issued	2022-12-19
dc.description.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.	en_US
dc.identifier.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.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115637
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40563
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Agile-Based Teaching	en_US
dc.subject	Machine Learning	en_US
dc.subject	Teaching Automation	en_US
dc.subject	EduOps	en_US
dc.subject	ML4Code	en_US
dc.subject.classification	Applied Mechanics	en_US
dc.subject.classification	Computer Science	en_US
dc.title	A Large Scale Agile Teaching Framework for Software Engineering	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Electrical & Computer	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US