Teaching Design Science as a Method for Effective Research Development
- URL: http://arxiv.org/abs/2407.09844v1
- Date: Sat, 13 Jul 2024 10:43:06 GMT
- Title: Teaching Design Science as a Method for Effective Research Development
- Authors: Oscar Pastor, Mmatshuene Anna Segooa, Jose Ignacio Panach,
- Abstract summary: 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.
- Score: 0.24578723416255752
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Applying Design Science Research (DSR) methodology is becoming a popular working resource for most Information Systems (IS) and Software engineering studies. The research and/or practical design problems that must be faced aim to answer the question of how to create or investigate an artifact in a given context. Precisely characterizing both artifact and context is essential for effective research development. While various design science guidelines and frameworks have been created by experts in IS engineering, emerging researchers and postgraduate students still find it challenging to apply this research methodology correctly. There is limited literature and materials that guide and support teaching novice researchers about the types of artifacts that can be developed to address a particular problem and decision-making in DSR. To address this gap in DSR, in this chapter, we explore DSR from an educational perspective, explaining both the concept of DSR and an effective method for teaching it. This chapter includes examples of DSR, a teaching methodology, learning objectives, and recommendations. Moreover, we have created a survey artifact intended to gather data on the experiences of design science users. The goal is to discover insights into contemporary issues and needs for DSR best practices (and, in consequence, evaluate the methodology we aim to teach). Our survey results offer a comprehensive overview of participants' experiences with DSR in the SE and IS Engineering domain, highlighting broad engagement primarily initiated during PhD studies and driven by supervisory guidance and research problems. We finally disclose the artifact to the community so that it can be used by other educators as a preparation when planning to teach DSR in tune with the experiences of their target audience.
Related papers
- StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs)
We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure.
Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
arXiv Detail & Related papers (2024-10-11T13:52:44Z) - 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) - On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains.
This paper presents a broad review of recent advances in element-wise ZSIR.
We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
arXiv Detail & Related papers (2024-08-09T05:49:21Z) - Deep Learning based Visually Rich Document Content Understanding: A Survey [8.788354139674789]
Visually Rich Documents (VRDs) are essential in academia, finance, medical fields, and marketing.
Deep learning has revolutionized this process, introducing models that leverage multimodal information vision, text, and layout.
These models have achieved state-of-the-art performance across various downstream tasks.
arXiv Detail & Related papers (2024-08-02T14:19:34Z) - A Process for Reviewing Design Science Research Papers to Enhance Content Knowledge & Research Opportunities [4.73194777046253]
Most published Information Systems research are of the behavioral science research (BSR) category rather than the design science research (DSR) category.
This is due in part to the BSR orientation of many IS doctoral programs, which often do not involve much technical courses.
We present a process for reviewing DSR papers that has as its objectives: enhancing technical content knowledge, increasing knowledge and understanding of approaches to designing and evaluating IS/IT artifacts, and facilitating the identification of new DSR opportunities.
arXiv Detail & Related papers (2024-07-24T17:40:00Z) - A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice [5.564583287027287]
Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions.
This survey reviews the progress in RS inclusively from 2017 to 2024.
It addresses challenges across various sectors, including e-commerce, healthcare, and finance.
arXiv Detail & Related papers (2024-07-18T17:00:53Z) - 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) - 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) - Systematic Mapping Protocol -- UX Design role in software development
process [55.2480439325792]
We present a systematic mapping protocol for investigating the role of the UX designer in the software development process.
We define the research questions, scope, sources, search strategy, selection criteria, data extraction, and analysis methods that we will use to conduct the mapping study.
arXiv Detail & Related papers (2024-02-20T16:56:46Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - Video Super Resolution Based on Deep Learning: A Comprehensive Survey [87.30395002197344]
We comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning.
We propose a taxonomy and classify the methods into six sub-categories according to the ways of utilizing inter-frame information.
We summarize and compare the performance of the representative VSR method on some benchmark datasets.
arXiv Detail & Related papers (2020-07-25T13:39:54Z)
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.