Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches

Novice programming environments: lowering the barriers, supporting the progression

In 2011, the author published an article that looked at the state of the art in novice programming environments. At the time, there had been an increase in the number of programming environments that were freely available for use by novice programmers, particularly children and young people. What was interesting was that they offered a relatively sophisticated set of development and support features within motivating and engaging environments, where programming could be seen as a means to a creative end, rather than an end in itself. Furthermore, these environments incorporated support for the social and collaborative aspects of learning. The article considered five environments—Scratch, Alice, Looking Glass, Greenfoot, and Flip— examining their characteristics and investigating the opportunities they might offer to educators and learners alike. It also considered the broader implications of such environments for both teaching and research. In this chapter, the author revisits the same five environments, looking at how they have changed in the intervening years. She considers their evolution in relation to changes in the field more broadly (e.g., an increased focus on “programming for all”) and reflects on the implications for teaching, as well as research and further development

The Role of Sonification as a Code Navigation Aid: Improving Programming Structure Readability and Understandability For Non-Visual Users

Integrated Development Environments (IDEs) play an important role in the workflow of many software developers, e.g. providing syntactic highlighting or other navigation aids to support the creation of lengthy codebases. Unfortunately, such complex visual information is difficult to convey with current screen-reader technologies, thereby creating barriers for programmers who are blind, who are nevertheless using IDEs. This dissertation is focused on utilizing audio-based techniques to assist non-visual programmers when navigating through large amounts of code. Recently, audio generation techniques have seen major improvements in their capabilities to covey visually-based information to both sighted and non-visual users – making them a potential candidate for providing useful information, especially in places where information is visually structured. However, there is little known about the usability of such techniques in software development. Therefore, we investigated whether audio-based techniques capable of providing useful information about the code structure to assist non-visual programmers. The major contributions in this dissertation are split into two major parts: The first part of this dissertation explains our prior work that investigates the major challenges in software development faced by non-visual programmers, specifically code navigation difficulties. It also discusses areas of improvement where additional features could be developed in order to make the programming environment more accessible to non-visual programmers. The second part of this dissertation focuses on studies aimed to evaluate the usability and efficacy of audio-based techniques for conveying the structure of the programming codebase, which was suggested by the stakeholders in Part I. Specifically, we investigated various sound effects, audio parameters, and different interaction techniques to determine whether these techniques could provide adequate support to assist non-visual programmers when navigating through lengthy codebases. In Part II, we discussed the methodological aspects of evaluating the above-mentioned techniques with the stakeholders and examine these techniques using an audio-based prototype that was designed to control audio timing, locations, and methods of interaction. A set of design guidelines are provided based on the evaluation described previously to suggest including an auditory-based feedback system in the programming environment in efforts to improve code structure readability and understandability for assisting non-visual programmers

An evolving approach to learning in problem solving and program development : the distributed learning model

Technological advances are paving the way for improvements in many sectors of society. The US education system needs to undergo a transformation of existing pedagogical methods to maximize utilization of new technologies. Traditional education has primarily been teacher driven, lectured-based in one location. Advances in technology are challenging existing paradigms by developing tools and educational environments that reach diverse learning styles and surpass the boundaries of current teaching methods. Distributed learning is an emerging paradigm today that has promise to contribute significantly to learning and improve overall academic success. This research first explores various systems that provide different modes of learning. The problem domain of this research is the difficulty novice programmers\u27 face when learning to program. This paper proposes how distributed learning can be used in a teaching environment to enrich learning and the impacts for the given problem domain

The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort

A meta-authoring tool for specifying behaviour in virtual reality environments

Includes bibliographical references (leaves 94-99).In this dissertation, we explore methods for empowering non-programmers with the ability to develop their own virtual environment applications. We explored some of the existing systems to determine what methodologies have already been successfully (or unsuccessfully) applied in the fields of virtual environment systems, authoring tools, and graphical user interfaces. From these methodologies we describe an ideal virtual environment authoring system with which comparisons may be drawn to evaluate existing systems. This ideal system represents a tool ideal in its ability to allow users of differing levels of skill to rapidly create virtual environment applications of any sophistication. Creating such a single, generic authoring tool for every different kind of application is, practically, an impossible task - more so if the authors are non-programmers. A more realistic solution to the problem would be to think of every environment as having a particular context such as a virtual museum or gallery. Creating authoring tools specific to these types of environment contexts greatly reduces the problem. We have therefore produced a progressive meta-authoring system that allows both novice and advanced users to create useful virtual reality applications, allowing the smooth migration of novice users to becoming more experienced. We believe that our system overcomes problems in architecture and support for novice users that can be found in many other authoring systems for virtual environments

Software Engineering in the IoT Context: Characteristics, Challenges, and Enabling Strategies

L'abstract è presente nell'allegato / the abstract is in the attachmen

8. Issues in Intelligent Computer-Assisted Instruction: Eval uation and Measurement

In this chapter we plan to explore two issues in the field of intelligent computer assisted instruction (ICAI) that we feel offer opportunities to advance the state of the art. These issues are evaluation of ICAI systems and the use of the underlying technology in ICAI systems to develop tests. For each issue we will provide a theoretical context, discuss key constructs, provide a brief window to the appropriate literature, suggest methodological solutions and conclude with a concrete example of the feasibility of the solution from our own research. INTELLIGENT COMPUTER-ASSISTED INSTRUCTION (ICAI) ICAI is the application of artificial intelligence to computer-assisted instruction. Artificial intelligence, a branch of computer science, is making computers smart in order to (a) make them more useful and (b) understand intelligence (Winston, 1977). Topic areas in artificial intelligence have included natural language processing (Schank, 1980), vision (Winston, 1975), knowledge representation (Woods, 1983), spoken language (Lea, 1980), planning (Hayes-Roth, 1980), and expert systems (Buchanan, 1981). The field of Artificial Intelligence (AI) has matured in both hardware and software. The most commonly used language in the field is LISP (List Processing). A major development in the hardware area is that personal LISP machines are now available at a relatively low cost (20-50K) with the power of prior mainframes. In the software area two advances stand out: (a) programming support environments such as LOOPS (Bobrow & Stefik, 1983) and (b) expert system tools. These latter tools are now running on powerful micros. The application of expert systems technology to a host of real-world problems has demonstrated the utility of artificial intelligence techniques in a very dramatic style. Expert system technology is the branch of artificial intelligence at this point most relevant to ICAI. Expert Systems Knowledge-based systems or expert systems are a collection of problem-solving computer programs containing both factual and experiential knowledge and data in a particular domain. When the knowledge embodied in the program is a result of a human expert elicitation, these systems are called expert systems. A typical expert system consists of a knowledge base, a reasoning mechanism popularly called an inference engine and a friendly user interface. The knowledge base consists of facts, concepts, and numerical data (declarative knowledge), procedures based on experience or rules of thumb (heuristics), and causal or conditional relationships (procedural knowledge). The inference engine searches or reasons with or about the knowledge base to arrive at intermediate conclusions or final results during the course of problem solving. It effectively decides when and what knowledge should be applied, applies the knowledge and determines when an acceptable solution has been found. The inference engine employs several problem-solving strategies in arriving at conclusions. Two of the popular schemes involve starting with a good description or desired solution and working backwards to the known facts or current situation (backward chaining), and starting with the current situation or known facts and working toward a goal or desired solution (forward chaining). The user interface may give the user choices (typically menu-driven) or allow the user to participate in the control of the process (mixed initiative). The interface allows the user: to describe a problem, input knowledge or data, browse through the knowledge base, pose question, review the reasoning process of the system, intervene as necessary, and control overall system operation. Successful expert systems have been developed in fields as diverse as mineral exploration (Duda & Gaschnig, 1981) and medical diagnosis (Clancy, 1981). ICAI Systems ICAI systems use approaches artificial intelligence and cognitive science to teach a range of subject matters. Representative types of subjects include: (a) collection of facts, for example, South American geography in SCHOLAR (Carbonell & Collins, 1973); (b) complete system models, for example, a ship propulsion system in STEAMER (Stevens & Steinberg, 1981) and a power supply in SOPHIE (Brown, Burton, & de Kleer, 1982); (c) completely described procedural rules, for example, strategy learning, WEST (Brown, Burton, & de Kleer, 1982), or arithmetic in BUGGY (Brown & Burton, 1978); (d) partly described procedural rules, for example, computer programming in PROUST (Johnson & Soloway, 1983); LISP Tutor (Anderson, Boyle, & Reiser, 1985); rules in ALGEBRA (McArthur, Stasz, & Hotta, 1987); diagnosis of infectious diseases in GUIDON (Clancey, 1979); and an imperfectly understood complex domain, causes of rainfall in WHY (Stevens, Collins, & Goldin, 1978). Excellent reviews by Barr and Feigenbaum (1982) and Wenger (1987) document many of these ICAI systems. Representative research in ICAI is described by O\u27Neil, Anderson, and Freeman (1986) and Wenger (1987). Although suggestive evidence has been provided by Anderson et al. (1985), few of these ICAI projects have been evaluated in any rigorous fashion. In a sense they have all been toy systems for research and demonstration. Yet, they have raised a good deal of excitement and enthusiasm about their likelihood of being effective instructional environments. With respect to cognitive science, progress has been made in the following areas: identification and analysis of misconceptions or bugs (Clement, Lockhead, & Soloway, 1980), the use of learning strategies (O\u27Neil & Spielberger, 1979; Weinstein & Mayer, 1986), expert versus novice distinction (Chi, Glaser, & Rees, 1982), the role of mental models in learning (Kieras & Bovair, 1983), and the role of self-explanations in problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1987). The key components of an ICAI system consist of a knowledge base: that is, (a) what the student is to learn; (b) a student model, either where the student is now with respect to subject matter or how student characteristics interact with subject matters, and (c) a tutor, that is, instructional techniques for teaching the declarative or procedural knowledge. These components are described in more detail by Fletcher (1985)