Bridging the Gap Between Research and Practice: The Agile Research Network

We report an action research-oriented approach to investigating agile project management methods which aims to bridge the gap between academic research and agile practice. We have set up a research network of academics from two universities, through which we run focussed project-based research into agile methods. Organisations are invited to suggest an ‘agile challenge’ and we work closely with them to investigate how challenge affects them. Our approach is both academic and practical. We use appropriate research methods such as interviews, observation and discussion to clarify and explore the nature of the challenge. We then undertake a detailed literature review to identify practical approaches that may be appropriate for adoption, and report our findings. If the organisation introduces new practices or approaches as a result of our work, we conduct an academic evaluation. Alternatively, if we uncover an under-researched area, we propose undertaking some basic research. As befits the topic, we work iteratively and incrementally and produce regular outputs. In this paper we introduce our approach, overview research methods used in the agile research literature, describe our research model, outline a case study, and discuss the advantages and disadvantages of our approach. We discuss the importance of producing outputs that are accessible to practitioners as well as researchers. Findings suggest that by investigating the challenges that organisations propose, we uncover problems that are of real relevance to the agile community and obtain rich insights into the facilitators and barriers that organisations face when using agile methods. Additionally, we find that practitioners are interested in research results as long as publications are relevant to their needs and are written accessibly. We are satisfied with the basic structure of our approach, but we anticipate that the method will evolve as we continue to work with collaborators

Empirical modelling principles to support learning in a cultural context

Much research on pedagogy stresses the need for a broad perspective on learning. Such a perspective might take account (for instance) of the experience that informs knowledge and understanding [Tur91], the situation in which the learning activity takes place [Lav88], and the influence of multiple intelligences [Gar83]. Educational technology appears to hold great promise in this connection. Computer-related technologies such as new media, the internet, virtual reality and brain-mediated communication afford access to a range of learning resources that grows ever wider in its scope and supports ever more sophisticated interactions. Whether educational technology is fulfilling its potential in broadening the horizons for learning activity is more controversial. Though some see the successful development of radically new educational resources as merely a matter of time, investment and engineering, there are also many critics of the trends in computer-based learning who see little evidence of the greater degree of human engagement to which new technologies aspire [Tal95]. This paper reviews the potential application to educational technology of principles and tools for computer-based modelling that have been developed under the auspices of the Empirical Modelling (EM) project at Warwick [EMweb]. This theme was first addressed at length in a previous paper [Bey97], and is here revisited in the light of new practical developments in EM both in respect of tools and of model-building that has been targetted at education at various levels. Our central thesis is that the problems of educational technology stem from the limitations of current conceptual frameworks and tool support for the essential cognitive model building activity, and that tackling these problems requires a radical shift in philosophical perspective on the nature and role of empirical knowledge that has significant practical implications. The paper is in two main sections. The first discusses the limitations of the classical computer science perspective where educational technology to support situated learning is concerned, and relates the learning activities that are most closely associated with a cultural context to the empiricist perspective on learning introduced in [Bey97]. The second outlines the principles of EM and describes and illustrates features of its practical application that are particularly well-suited to learning in a cultural setting

Clients’ participation in software projects: comparative case study between an agile and a ‘traditional’ software company

One of the main characteristics of agile software development is the active and continuous participation and involvement of the clients throughout the project. According to agile proponents, this leads to building ‘the right’ product and to satisfied clients. In this paper we present a comparative study of two Dutch software development companies in respect to client participation and its impact on the project. One of the companies is purely agile while the other is following a traditional software development approach. Our study suggests that active clients’ participation is not an exclusive attribute of agile projects and that it can be successfully integrated (and implemented) in a traditional project as well. Further, the study shows that by involving clients, software companies have the chance to get higher customer satisfaction, regardless whether or not they implement agile software development processes. Although our study is not quantitative, we think that it is indicative about the impact of the factor “client’s participation” on the client’s satisfaction

Bridging the gap between research and agile practice: an evolutionary model

There is wide acceptance in the software engineering field that industry and research can gain significantly from each other and there have been several initiatives to encourage collaboration between the two. However there are some often-quoted challenges in this kind of collaboration. For example, that the timescales of research and practice are incompatible, that research is not seen as relevant for practice, and that research demands a different kind of rigour than practice supports. These are complex challenges that are not always easy to overcome. Since the beginning of 2013 we have been using an approach designed to address some of these challenges and to bridge the gap between research and practice, specifically in the agile software development arena. So far we have collaborated successfully with three partners and have investigated three practitioner-driven challenges with agile. The model of collaboration that we adopted has evolved with the lessons learned in the first two collaborations and been modified for the third. In this paper we introduce the collaboration model, discuss how it addresses the collaboration challenges between research and practice and how it has evolved, and describe the lessons learned from our experience

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering