VRBridge: a Constructivist Approach to Supporting Interaction Design and End-User Authoring in Virtual Reality

For any technology to become widely-used and accepted, it must support end-user authoring and customisation. This means making the technology accessible by enabling understanding of its design issues and reducing its technical barriers. Our interest is in enabling end-users to author dynamic virtual environments (VEs), specifically their interactions: player interactions with objects and the environment; and object interactions with each other and the environment. This thesis describes a method to create tools and design aids which enable end-users to design and implement interactions in a VE and assist them in building the requisite domain knowledge, while reducing the costs of learning a new set of skills. Our design method is based in constructivism, which is a theory that examines the acquisition and use of knowledge. It provides principles for managing complexity in knowledge acquisition: multiplicity of representations and perspectives; simplicity of basic components; encouragement of exploration; support for deep reflection; and providing users with control of their process as much as possible. We derived two main design aids from these principles: multiple, interactive and synchronised domain-specific representations of the design; and multiple forms of non-invasive and user-adaptable scaffolding. The method began with extensive research into representations and scaffolding, followed by investigation of the design strategies of experts, the needs of novices and how best to support them with software, and the requirements of the VR domain. We also conducted a classroom observation of the practices of non-programmers in VR design, to discover their specific problems with effectively conceptualising and communicating interactions in VR. Based on our findings in this research and our constructivist guidelines, we developed VRBridge, an interaction authoring tool. This contained a simple event-action interface for creating interactions using trigger-condition-action triads or Triggersets. We conducted two experimental evaluations during the design of VRBridge, to test the effectiveness of our design aids and the basic tool. The first tested the effectiveness of the Triggersets and additional representations: a Floorplan, a Sequence Diagram and Timelines. We used observation, interviews and task success to evaluate how effectively end-users could analyse and debug interactions created with VRBridge. We found that the Triggersets were effective and usable by novices to analyse an interaction design, and that the representations significantly improved end-user work and experience. The second experiment was large-scale (124 participants) and conducted over two weeks. Participants worked on authoring tasks which embodied typical interactions and complexities in the domain. We used a task exploration metric, questionnaires and computer logging to evaluate aspects of task performance: how effectively end-users could create interactions with VRBridge; how effectively they worked in the domain of VR authoring; how much enjoyment or satisfaction they experienced during the process; and how well they learned over time. This experiment tested the entire system and the effects of the scaffolding and representations. We found that all users were able to complete authoring tasks using VRBridge after very little experience with the system and domain; all users improved and felt more satisfaction over time; users with representations or scaffolding as a design aid completed the task more expertly, explored more effectively, felt more satisfaction and learned better than those without design aids; users with representations explored more effectively and felt more satisfaction than those with scaffolding; and users with both design aids learned better but did not improve in any other way over users with a single design aid. We also gained evidence about how the scaffolding, representations and basic tool were used during the evaluation. The contributions of this thesis are: an effective and efficient theory-based design method; a case study in the use of constructivism to structure a design process and deliver effective tools; a proof-of-concept prototype with which novices can create interactions in VR without traditional programming; evidence about the problems that novices face when designing interactions and dealing with unfamiliar programming concepts; empirical evidence about the relative effectiveness of additional representations and scaffolding as support for designing interactions; guidelines for supporting end-user authoring in general; and guidelines for the design of effective interaction authoring systems in general

Gender differences in end-user debugging strategies

There has been little prior research reporting strategy usage in end-user problem solving, and even less using gender as a factor. Without this type of information, enduser programming systems cannot know the “target” at which to aim, if they are to support male and female end-user programmers’ debugging. As a background to the thesis, an experiment was conducted by our group members, where the participants were given a post session questionnaire that had an open-ended question about what debugging strategies they adopted in finding and fixing errors. It was found that among the mentioned strategies, testing and code inspection had significant statistical differences among male and/or female success groups. This thesis’s goal is the investigation of the behavioral evidence of the two primary strategies, testing and code-inspection using gender as a factor. Using quantitative and qualitative methods, we analyzed the two strategies reported, and looked for relationships among participants’ strategy choices, gender, and debugging success. Our results indicate that males and females debug in quite different ways, and the debugging strategies that worked well for the males were not the same ones that worked well for the females. Our results also reveal that tools currently available to end-user debuggers may be especially deficient in supporting debugging strategies used by females

MevaL: A Visual Machine Learning Model Evaluation Tool for Financial Crime Detection

Data Science and Machine Learning are two valuable allies to fight financial crime,the domain where Feedzai seeks to leverage its value proposition in support of its mission:to make banking and commerce safe. Data is at the core of both fields and this domain, sostructuring instances for visual consumption provides an effective way of understandingthe data and communicating insights.The development of a solution for each project and use case requires a careful andeffective Machine Learning Model Evaluation stage, as it is the major source of feedbackbefore deployment. The tooling for this stage available at Feedzai can be improved,accelerated, visually supported, and diversified to enable data scientists to boost theirdaily work and the quality of the models.In this work, I propose to collect and compile internal and external input, in terms ofworkflow and Model Evaluation, in a proposal hierarchically segmented by well-definedobjectives and tasks, to instantiate the proposal in a Python package, and to iteratively val-idate the package with Feedzai’s data scientists. Therefore, the first contribution is MevaL,a Python package for Model Evaluation with visual support, integrated into Feedzai’s DataScience environment by design. In fact, MevaL is already being leveraged as a visualization package on two internal reporting projects that are serving some of Feedzai’s majorclients.In addition to MevaL, the second contribution of this work is the Model EvaluationTopology developed to ensure clear communication and design of features.A Ciência de Dados e a Aprendizagem Automática [277] são duas valiosas aliadas no combate à criminalidade económico-financeira, o domínio em que a Feedzai procura potenciar a sua proposta de valor em prol da sua missão: tornar o sistema bancário e o comércio seguros. Além disso, os dados estão no centro das duas áreas e deste domínio.Assim, a estruturação visual dos mesmos fornece uma maneira eficaz de os entender e transmitir informação.O desenvolvimento de uma solução para cada projeto e caso de uso requer um estágiocuidadoso e eficaz de Avaliação de Modelos de Aprendizagem Automática, pois esteestágio coincide com a principal fonte de retorno (feedback) antes da implementaçãoda solução. As ferramentas de Avaliação de Modelos disponíveis na Feedzai podem seraprimoradas, aceleradas, suportadas visualmente e diversificadas para permitir que oscientistas de dados impulsionem o seu trabalho diário e a qualidade destes modelos.Neste trabalho, proponho a recolha e compilação de informação interna e externa, em termos de fluxo de trabalho e Avaliação de Modelos, numa proposta hierarquicamente segmentada por objetivos e tarefas bem definidas, a instanciação desta proposta num pacote Python e a validação iterativa deste pacote em colaboração com os cientistas de dados da Feedzai. Posto isto, a primeira contribuição deste trabalho é o MevaL, um pacote Python para Avaliação de Modelos com suporte visual, integrado no ambiente de Ciência de Dados da Feedzai. Na verdade, o MevaL já está a ser utilizado como um pacote de visualização em dois projetos internos de preparação de relatórios automáticos para alguns dos principais clientes da Feedzai.Além do MevaL, a segunda contribuição deste trabalho é a Topologia de Avaliação de Modelos desenvolvida para garantir uma comunicação clara e o design enquadrado das diferentes funcionalidades

Automating Electronic Health Record Data Quality Assessment

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown

Facilitating the modelling and automated analysis of cryptographic protocols

Includes bibliographical references.Multi-dimensional security protocol engineering is effective for creating cryptographic protocols since it encompasses a variety of design, analysis and deployment techniques, thereby providing a higher level of confidence than individual approaches. SPEAR II, the Security Protocol Engineering and Analysis Resource n, is a protocol engineering tool built on the foundation of previous experience garnered during the SPEAR I project in 1997. The goal of the SPEAR II tool is to facilitate cryptographic protocol engineering and aid users in distilling the critical issues during an engineering session by presenting them with an appropriate level of detail and guiding them as much as possible. The SPEAR II tool currently consists of four components that have been created as part of this dissertation and integrated into one consistent and unified graphical interface: a protocol specification environment (GYPSIE), a GNY statement construction interface (Visual GNY), a Prolog-based GNY analysis engine (GYNGER) and a message rounds calculator

The development of design guidelines for educational programming environments

Introductory programming courses at university are currently experiencing a significant dropout and failure rate. Whilst several reasons have been attributed to these numbers by researchers, such as cognitive factors and aptitude, it is still unclear why programming is a natural skill for some students and a cause of struggle for others. Most of the research in the computer science literature suggests that methods of teaching programming and students’ learning styles as reasons behind this trend. In addition to the choice of the first programming language taught. With the popularity of virtual learning environments and online courses, several instructors are incorporating these e-learning tools in their lectures in an attempt to increase engagement and achievement. However, many of these strategies fail as they do not use effective teaching practices or recognise the learning preferences exhibited by a diverse student population. Therefore this research proposes that combining multiple teaching methods to accommodate different learners' preferences will significantly improve performance in programming. To test the hypothesis, an interactive web based learning tool to teach Python programming language (PILeT) was developed. The tool’s novel contribution is that it offers a combination of pedagogical methods to support student’s learning style based on the Felder-Silverman model. First, PILeT was evaluated by both expert and representative users to detect any usability or interface design issues that might interfere with students’ learning. Once the problems were detected and fixed, PILeT was evaluated again to measure the learning outcomes that resulted from its use. The experimental results show that PILeT has a positive impact on students learning programming

Augmenting IDEs with Runtime Information for Software Maintenance

Object-oriented language features such as inheritance, abstract types, late-binding, or polymorphism lead to distributed and scattered code, rendering a software system hard to understand and maintain. The integrated development environment (IDE), the primary tool used by developers to maintain software systems, usually purely operates on static source code and does not reveal dynamic relationships between distributed source artifacts, which makes it difficult for developers to understand and navigate software systems. Another shortcoming of today's IDEs is the large amount of information with which they typically overwhelm developers. Large software systems encompass several thousand source artifacts such as classes and methods. These static artifacts are presented by IDEs in views such as trees or source editors. To gain an understanding of a system, developers have to open many such views, which leads to a workspace cluttered with different windows or tabs. Navigating through the code or maintaining a working context is thus difficult for developers working on large software systems. In this dissertation we address the question how to augment IDEs with dynamic information to better navigate scattered code while at the same time not overwhelming developers with even more information in the IDE views. We claim that by first reducing the amount of information developers have to deal with, we are subsequently able to embed dynamic information in the familiar source perspectives of IDEs to better comprehend and navigate large software spaces. We propose means to reduce or mitigate the information by highlighting relevant source elements, by explicitly representing working context, and by automatically housekeeping the workspace in the IDE. We then improve navigation of scattered code by explicitly representing dynamic collaboration and software features in the static source perspectives of IDEs. We validate our claim by conducting empirical experiments with developers and by analyzing recorded development sessions

Foodlocker: an Online Marketplace for Automated Meals

Food waste is a major problem for the human race, particularly in "rich" industrial countries.We propose a creative, partial solution to this problem through the development of an online marketplace for left-over food and a physical infrastructure to secure the food throughout each transaction.The software platform will consist of three separate portions: a mobile food consumer application, a mobile food provider application, and a web food provider application. Restaurants and other licensed food distributors can utilize the food provider applications to broadcast available left-over meals, which can be purchased at a discounted price. The hardware infrastructure will be composed of "food lockers," refrigerated units that automatically dispense food to consumers.As this vision is ambitious, the goal of my thesis was to develop the software platform and a small prototype with two individual lockers to demonstrate the functionality of the entire system. An experiment was constructed to validate the correctness of multiple, synchronous calls to consumer/provider functions. Results indicate that this system is near ready for small scale deployment

Graphical scaffolding for the learning of data wrangling APIs

In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction