A systematic literature review of capstone courses in software
engineering
- URL: http://arxiv.org/abs/2301.03554v1
- Date: Mon, 9 Jan 2023 18:04:35 GMT
- Title: A systematic literature review of capstone courses in software
engineering
- Authors: Saara Tenhunen, Tomi M\"annist\"o, Matti Luukkainen, Petri Ihantola
- Abstract summary: capstone projects are a common way to provide students with hands-on experience and teach soft skills.
This paper explores the characteristics of software engineering capstone courses presented in the literature.
- Score: 0.3536605202672354
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tertiary education institutions aim to prepare their computer science and
software engineering students for working life. While much of the technical
principles are covered in lower-level courses, team-based capstone projects are
a common way to provide students with hands-on experience and teach soft
skills. This paper explores the characteristics of software engineering
capstone courses presented in the literature. The goal of this work is to
understand the pros and cons of different approaches by synthesising the
various aspects of software engineering capstone courses and related
experiences. In a systematic literature review for 2007-2022, we identified 127
primary studies. These studies were analysed based on their presented course
characteristics and the reported course outcomes. The characteristics were
synthesised into a taxonomy consisting of duration, team sizes, client and
project sources, project implementation, and student assessment. We found out
that capstone courses generally last one semester and divide students into
groups of 4-5 where they work on a project for a client. For a slight majority
of courses, the clients are external to the course staff and students are often
expected to produce a proof-of-concept level software product as the main end
deliverable. The courses also offer versatile assessments for students
throughout the project. This paper provides researchers and educators with a
classification of characteristics of software engineering capstone courses
based on previous research. We further synthesise insights on the reported
outcomes of capstone courses. Our review study aims to help educators to
identify various ways of organising capstones and effectively plan and deliver
their own capstone courses. The characterisation also helps researchers to
conduct further studies on software engineering capstones.
Related papers
- Ten Years of Teaching Empirical Software Engineering in the context of Energy-efficient Software [12.26887943861433]
We share our experience in running ten editions of the Green Lab course at the Vrije Universiteit Amsterdam, the Netherlands.
The course is given in the Software Engineering and Green IT track of the Computer Science Master program of the VU.
arXiv Detail & Related papers (2024-07-08T07:44:49Z) - Teaching and Learning Ethnography for Software Engineering Contexts [1.0992151305603264]
This chapter provides an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students.
The contents of the chapter focus on what we think is the core basic knowledge for newbies to ethnography as a research method.
The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.
arXiv Detail & Related papers (2024-07-05T15:43:02Z) - Enhancing Student Engagement in Large-Scale Capstone Courses: An Experience Report [2.7629502923028944]
capstone courses offer students a valuable opportunity to gain hands-on experience in software development.
coordinating a capstone course, especially for a large student cohort, can be a daunting task for academic staff.
We outline the iterative development and refinement of our capstone course as it grew substantially in size over a span of six consecutive sessions.
arXiv Detail & Related papers (2024-04-03T23:59:35Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Personalization, Cognition, and Gamification-based Programming Language
Learning: A State-of-the-Art Systematic Literature Review [0.13053649021965597]
Programming courses in computing science are important because they are often the first introduction to computer programming for many students.
The current teacher-lecturer model of learning commonly employed in university lecture halls often results in a lack of motivation and participation in learning.
This paper provides insights into designing and implementing effective personalized gamification interventions in programming courses.
arXiv Detail & Related papers (2023-09-05T05:14:23Z) - Team Composition in Software Engineering Education [0.5439020425819]
The study presented in this paper aims to better understand the student team composition in software engineering education.
The initial findings of the ongoing Action research study are presented.
arXiv Detail & Related papers (2023-06-14T11:00:05Z) - Using Machine Learning to Predict Engineering Technology Students'
Success with Computer Aided Design [50.591267188664666]
We show how data combined with machine learning techniques can predict how well a particular student will perform in a design task.
We found that our models using early design sequence actions are particularly valuable for prediction.
Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
arXiv Detail & Related papers (2021-08-12T20:24:54Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Are Top School Students More Critical of Their Professors? Mining
Comments on RateMyProfessor.com [83.2634062100579]
Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students.
Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
arXiv Detail & Related papers (2021-01-23T20:01:36Z) - Data Science for Engineers: A Teaching Ecosystem [59.00739310930656]
We describe an ecosystem for teaching data science to engineers at the Faculty of Physical and Mathematical Sciences, Universidad de Chile.
This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments.
By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems.
arXiv Detail & Related papers (2021-01-14T14:17:57Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.