Teaching Action Research

• URL: http://arxiv.org/abs/2408.02399v1
• Date: Mon, 5 Aug 2024 11:44:49 GMT
• Title: Teaching Action Research
• Authors: Miroslaw Staron,
• Abstract summary: Action research entered into software engineering as one of the responses to the software engineering research crisis at the end of the last millennium. This chapter describes the pillars of action research as a methodology and how to teach them.
• Score: 2.465689259704613
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Action research entered into software engineering as one of the responses to the software engineering research crisis at the end of the last millennium. As one of the challenges in the crisis was the lack of empirical results and the transfer of research results into practices, action research could address these challenges. It is a methodology where collaboration and host organizations are the focus of knowledge discovery, development, and documentation. Although the method is often well received in industrial contexts, it isn't easy to learn as it requires experience and varies from organization to organization. This chapter describes the pillars of action research as a methodology and how to teach them. The chapter includes examples of teaching action research at the bachelor, master, and PhD levels. In addition to theory, the chapter contains examples from practice.

Related papers

• O1 Replication Journey: A Strategic Progress Report -- Part 1 [52.062216849476776]
  This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects. We propose the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process.
  arXiv  Detail & Related papers  (2024-10-08T15:13:01Z)
• Teaching Design Science as a Method for Effective Research Development [0.24578723416255752]
  Applying Design Science Research (DSR) methodology is becoming a popular working resource for Information Systems (IS) and Software engineering studies. This chapter includes examples of DSR, a teaching methodology, learning objectives, and recommendations. We have created a survey artifact intended to gather data on the experiences of design science users.
  arXiv  Detail & Related papers  (2024-07-13T10:43:06Z)
• Teaching Research Design in Software Engineering [1.9659095632676098]
  Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies. This chapter teaches foundational skills in research design, essential for educating software engineers and researchers in ESE.
  arXiv  Detail & Related papers  (2024-07-06T21:06:13Z)
• Teaching Empirical Methods at Eindhoven University of Technology [55.17240664632298]
  We discuss the challenges of teaching a course on research methods. We share our lessons learned and the do's and don'ts we learned over several iterations of teaching the course.
  arXiv  Detail & Related papers  (2024-07-05T17:14:08Z)
• Action Research with Industrial Software Engineering -- An Educational Perspective [0.5257115841810258]
  Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings. It requires a different kind of interaction with the software development organisation. This chapter is intended to support learning and teaching action research.
  arXiv  Detail & Related papers  (2024-07-05T17:05:06Z)
• ResearchBot: Bridging the Gap between Academic Research and Practical Programming Communities [1.7825757481227438]
  This project introduces ResearchBot, a tool designed to bridge the academia-industry gap. ResearchBot employs a modular approach, encompassing understanding questions, curating queries to obtain relevant papers and summarizing paper content. The core objective of ResearchBot is to democratize access to academic knowledge for industry professionals.
  arXiv  Detail & Related papers  (2024-07-02T20:17:13Z)
• DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
  We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
  arXiv  Detail & Related papers  (2024-06-10T20:08:44Z)
• Towards machine learning guided by best practices [0.0]
  Machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE) This thesis aims to answer research questions that help to understand the practices used and discussed by practitioners and researchers in the SE community.
  arXiv  Detail & Related papers  (2023-04-29T10:58:37Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation [63.18666008322476]
  Machine learning methods are being increasingly applied in sensitive societal contexts. The present case study has two main objectives. First, to expose these challenges and how they affect the use of relevant and novel explanations methods. And second, to present a set of strategies that mitigate such challenges, as faced when implementing explanation methods in a relevant application domain.
  arXiv  Detail & Related papers  (2021-04-09T01:54:58Z)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

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.