An exploratory study of experiences of design at hackathons

Hackathons are popular events where participants “hack” together a project, from ideation through to the final presentation, in 24-48 hours. It is typical for hackathons to be tech-centric; therefore, the attendees tend to be mostly computer programmers, designers, and engineers. Hackathons have been hosted by communities, corporations, and educational institutions with goals of producing artifacts or products, networking, and learning. Hackathons are inherently collaborative, as attendees typically work together in small groups. The participants depend on team skills to complete their projects in the short event time frame. Hackathons hold a lot of potential for design research; however, the community has been slow to recognize the potential research opportunities. The existing research on design at hackathons is limited, despite the consideration of hackathons as a setting where participants engage in design activity. Motivated by the popularity of hackathons and the lack of significant research on design at hackathons, this thesis presents an exploratory research of hackathon participants’ experiences of design at these unique events. The research is motivated by three main research questions: 1) what are the characteristics of the design process followed by hackathon participants, 2) how does the design process at hackathons differ from more typical design projects, and 3) how does team composition impact the design experience at hackathons? To answer these research questions, 16 semi-structured interviews were conducted with participants who had collectively participated in 65 hackathons. The transcripts of the interviews underwent a thematic analysis, a methodology that identifies themes in the dataset. Codes were assigned to interview transcripts, resulting in 90 codes on 684 excerpts. The codes were then clustered thematically to identify five major themes: the typical hackathon experience, design at hackathons, collaboration, evaluation of hackathons, and miscellaneous. These themes informed the findings of the study and their presentation in this thesis. Interview results suggest that the main stages of the design process at hackathons are ideation, building, and pitch preparation and delivery. While some research, including user research, and design iteration activities may occur, the short time frame of the hackathon severely limits these activities and forces participants to instead prioritize the building phase. Further, due to the continuous nature of the event, participants are not able to take significant breaks from their design tasks, thus not benefiting from potential incubation periods. It is concluded that while hackathons share many characteristics of the design process with more typical design projects, the nature of these events causes the design processes to be adapted in significant ways. Team composition is found to be highly influential in the projects and processes of hackathon teams. Participants’ motivations for attending hackathons - to win, network, learn, or have fun - play a role in what activities they participate in at the events and how they approach their hackathon design project. Motivations for attending, along skills and interest are an important factor considered in team formation. Hackathon teams tend to comprise of three roles: developer, designer, and business analyst, which are determined based on knowledge and experience. The interviews reveal conflict between developers and designers based on their desired approaches to the hackathon design process. Whereas developers are eager to begin building almost immediately, designers encourage a more thorough progression through the design process. The contributions of this thesis hold implications for hackathon participants and organizers, design researchers, and design educators. The research frames hackathons as design-centered settings that generate rich data. As an exploratory study, the research builds a foundational understanding of design at hackathons, offering a new direction for design research and prompting a number of future research opportunities

Interactive Machine Learning for User-Innovation Toolkits – An Action Design Research approach

Machine learning offers great potential to developers and end users in the creative industries. However, to better support creative software developers' needs and empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. This thesis asks the following research questions: How can we apply a user-centred approach to the design of developer tools for rapid prototyping with Interactive Machine Learning? In what ways can we design better developer tools to accelerate and broaden innovation with machine learning? This thesis presents a three-year longitudinal action research study that I undertook within a multi-institutional consortium leading the EU H2020 -funded Innovation Action RAPID-MIX. The scope of the research presented here was the application of a user-centred approach to the design and evaluation of developer tools for rapid prototyping and product development with machine learning. This thesis presents my work in collaboration with other members of RAPID-MIX, including design and deployment of a user-centred methodology for the project, interventions for gathering requirements with RAPID-MIX consortium stakeholders and end users, and prototyping, development and evaluation of a software development toolkit for interactive machine learning. This thesis contributes with new understanding about the consequences and implications of a user-centred approach to the design and evaluation of developer tools for rapid prototyping of interactive machine learning systems. This includes 1) new understanding about the goals, needs, expectations, and challenges facing creative machine-learning non-expert developers and 2) an evaluation of the usability and design trade-offs of a toolkit for rapid prototyping with interactive machine learning. This thesis also contributes with 3) a methods framework of User-Centred Design Actions for harmonising User-Centred Design with Action Research and supporting the collaboration between action researchers and practitioners working in rapid innovation actions, and 4) recommendations for applying Action Research and User-Centred Design in similar contexts and scale

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Designing social media analytics tools to support non-market institutions: Four case studies using Twitter data

This research investigates the design of social media tools for non-market institutions, such as local government or community groups. At the core of this practice-based research is a software tool called LocalNets. LocalNets was developed to collect, analyse and visualise data from Twitter, thereby revealing information about community structure and community assets. It is anticipated that this information could help non- market institutions and the communities with which they work. Twitter users send messages to one another using the ‘@mention’ function. This activity is made visible publicly and has the potential to indicate a Twitter user’s participation in a ‘community structure’; that is, it can reveal an interpersonal network of social connections. Twitter activity also provides data about community assets (such as parks, shops and cinemas) when tweets mention these assets’ names. The context for this research is the Creative Exchange Hub (CX), one of four Knowledge Exchange Hubs for the Creative Economy funded by the UK Arts and Humanities Research Council (AHRC). Under the theme of ‘Digital Public Space’, the CX Hub facilitated creative research collaborations between PhD researchers, academics and non-academic institutions. Building on the CX model, this PhD research forged partnerships between local councils, non-public sector institutions that work with communities, software developers and academics with relevant subject expertise. Development of the LocalNets tool was undertaken as an integral part of the research. As the software was developed, it was deployed in relevant contexts through partnerships with a range of non-market institutions, predominantly located in the UK, to explore its use in those contexts. Four projects are presented as design case studies: 1) a prototyping phase, 2) a project with the Royal Society of Arts in the London Borough of Hounslow, 3) a multi-partner project in Peterborough, and 4) a project with Newspeak House, a technology and politics co-working space located in London. The case studies were undertaken using an Action Design Research method, as articulated by Sein et al. Findings from these case studies are grouped into two categories. The first are ‘Implementation findings’ which relate specifically to the use of data from Twitter. Second there are six ‘situated design principles’ which were developed across the case studies, and which are proposed as having potential application beyond Twitter data. The ‘Implementation findings’ include that Twitter can be effective for locating participants for focus groups on community topics, and that the opinions expressed directly in tweets are rarely sufficient for the local government of community groups to respond to. These findings could benefit designers working with Twitter data. The six situated design principles were developed through the case studies: two apply Burt’s brokerage social capital theory, describing how network structure relates to social capital; two apply Donath’s signalling theory – which suggests how social media behaviours can indicate perceptions of community assets; and two situated design principles apply Borgatti and Halgin’s network flow model – a theory which draws together brokerage social capital and signalling theory. The principles are applicable to social media analytics tools and are relevant to the goals of non-market institutions. They are situated in the context of the case studies; however, they are potentially applicable to social media platforms other than Twitter. Linders identifies a paucity of research into social media tools for non-market institutions. The findings of this research, developed by deploying and testing the LocalNets social media analytics tool with non-market institutions, aim to address that research gap and to inform practitioner designers working in this area

Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems

We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies -- qualitative interviews, a controlled experiment, and a card-sorting task -- to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.Comment: IUI 202

Handbook transdisciplinary learning

What is transdisciplinarity and what are its methodß How does a living lab work? What is the purpose of citizen science, student-organized teaching and cooperative education? This handbook unpacks key terms and concepts to describe the range of transdisciplinary learning in the context of academic education. Transdisciplinary learning turns out to be a comprehensive innovation process in response to the major global challenges such as climate change, urbanization or migration. A reference work for students, lecturers, scientists, and anyone wanting to understand the profound changes in higher education

Handbook Transdisciplinary Learning

What is transdisciplinarity - and what are its methods? How does a living lab work? What is the purpose of citizen science, student-organized teaching and cooperative education? This handbook unpacks key terms and concepts to describe the range of transdisciplinary learning in the context of academic education. Transdisciplinary learning turns out to be a comprehensive innovation process in response to the major global challenges such as climate change, urbanization or migration. A reference work for students, lecturers, scientists, and anyone wanting to understand the profound changes in higher education

Co-creative events for engagement with digital cultural heritage collections

More and more cultural heritage institutions digitise their collections aiming to unlock potentials of accessibility, usefulness, and meaning to a new variety of users. However, there is a lack of robust knowledge about what exactly enables successful engagement with digital collections. This thesis aims to narrow this gap by investigating the relationships of three stakeholder groups participating in co-creative use of collections: museum practitioners, active users, and Open GLAM community members. It focuses on three main research questions: • What are the challenges and benefits of co-creative events for these stakeholders? • How do these stakeholders collaborate and what are the factors impacting their collaboration? • What is the role of digital reuse and creative practice in engaging users with cultural heritage collections? These questions are examined in three co-creative events: a hackathon about discomforting objects on display at The Hunterian, University of Glasgow, Scotland; a Coding da Vinci hackathon with openly licensed digital cultural collections in Dortmund, Germany; and a workshop for remixing museum objects with digital tools at the Museum Europäischer Kulturen, Berlin, Germany. The methodology focuses on participants’ collaborative and creative processes and combines ethnographic methods with practice research, using participant observation, creative workshops, interviews, and surveys. The research provides multi-faceted insights into reusing digital collections and highlights the crucial role of social motivations, media practices, and institutional contexts for engagement. The findings suggest that, in order to unfold the social potential of collections, digitisation needs to be complemented with socio-affective spaces in which diverse participants can develop relationships, negotiate meanings, and explore uses of cultural heritage. The thesis thus outlines practice-oriented approaches for effectively supporting these processes. It forms part of the POEM European Training Network on participatory memory practices funded by the EU Horizon 2020 Marie Skłodowska-Curie programme

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data