Conference paper 2021

Experience Report on Improving Software Engineering Education

4th International Conference on Innovative Computing, ICIC 2021
Conference
Abstract

Improving and adapting strategies to teach Software Engineering (SE) insights is one of the primary goals of SE schools. Every year, we come up with plenty of studies that provide suggestions and guidelines on improving teaching methods, problem-solving strategies, and curriculum design. On the other hand, we do not know how the world's top-notch universities focus on the process and academic improvement. This study shares our experience while investigating the Tsinghua University (QS World ranked 17)1, School of Software process and providing an initial guideline for further improvement. In this study, we, first, aim to gather suggestions and guidelines given in the literature. Secondly, we observe and analyze the feedback given by external reviewers and, as an example, map those feedback-augmented findings and suggestions onto school processes. Lastly, we suggest how the software school can further enhance the teaching strategies, curriculum design, and problem solving skills. © 2021 IEEE.

Keywords

Author Keywords

education Case study curriculum design Literature review Software Engineering Academic process

Index Keywords

Curricula Engineering education Case-studies Teaching methods Problem-solving Curricula design Software engineering education Software engineering Literature reviews Technology transfer Engineering schools Strategy designs Academic process Experience report
Author Affiliations
School of Software, Tsinghua University, Beijing, China
Funding & Acknowledgements
Grant: 62021002
Funding text 1: Financial support from Beijing Key Lab of Industrial Big Data Systems and Applications, Tsinghua University Big Data Research Center, and Science Fund for Creative Research Groups of the Natural Science Foundation of China Project no. 62021002 are greatly Acknowledgement. 1https://www.topuniversities.com/universities/tsinghua-university; Funding text 2: ACKNOWLEDGEMENT Financial support from Beijing Key Lab of Industrial Big Data Systems and Applications, Tsinghua University Big Data Research Center, and Science Fund for Creative Research Groups of the Natural Science Foundation of China Project no. 62021002 are greatly Acknowledgement.
References 10 References
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