Next to knowledge about the design methodologies – lab, field, showroom – to Constructive Design Research, I got acquainted with Machine Learning by choosing the topic ‘physicalization’. Getting a first-hand experience in the lab methodology I came to understand how it contributes to design in a way that minimizes complexity of a design context through a controlled environment by defining, if possible, all possible aspects of the design context. It was both challenging and interesting to consider methods for data collection and corresponding data analysis into consideration, which made me realize I wanted to become a design researcher.

Constructive Design Research

While designing behavioral policy by interpreting empirical findings in cognitive science, I became acquainted with the fields’ classical findings, paradigms and theoretical perspectives. Specifically, the course materials on ‘mental representations’ and ‘similarity’ influenced how I integrate my vision in relation to designing for enhanced understanding and have formed a baseline for how I perceive user understanding and approach these theoretically in relation to my vision. It further enabled me to consider possible applications of scientific knowledge to societal problems, through which I gained perspective which allows me to have a constructive critical attitude towards ongoing design research.

Advanced Cognition

Equipped with basic understanding through Constructive Design Research, I learned a great deal about how designing with machine learning influences design processes. That is, in order to make resourceful use of machine learning algorithms, it is essential to collect a good amount of data on various attributes early on. Moreover, evaluation of data sources in terms of how much can be accessed and whether it is consistent proved valuable considerations in my work and formed a baseline to my growth in Math, Data & Computing.

Aligning what I have learned through this course with my vision and identity has been of great influence to what I wanted to achieve during my Masters; it was after this course that Natural Language Processing was on my radar. Emphasizing this is clear through the following excerpt from my reflection after the course:

Designing with Advanced AI

While positioning itself far from my interests, being part of the mobility squad enabled me to further develop my vision in scope of user centered design processes. I learned that designing for understanding unfolds in an aim to facilitate self-reflection, rather than self-expression, when a single user is considered.

In terms of professional skills, I learned to trust my competence in Technology & Realization and support my creative ideas with arguments that proof technical feasibility. I learned how to communicate and collaborate on the integration of hardware devices and took charge to ensure a fully functional and experiential prototype. In collaborating with a business-oriented team member, I came to realize that in moving from prototype to production, economic feasibility should be considered as well.

Master Project 1: AVTR HOAN

Through the materials provided in the course, I got acquainted with the broad variety of paradigms, approaches and methods to conducting design research, enhancing my overall competence in conducting Design and Research Processes. Privileged by a team possessing strong organizational skills, I finally managed to get a better grip on carefully planning and conducting a research project and improved in reasoning about research, writing, reporting and dealing with scientific information.

Although I was infinitely intrigued by the method and approach we went for, analyzing the data to our qualitative research was exhausting. Intense as it appeared to be, the time-intensive experience prompted the goal to collect qualitative data through research prototypes and look for ways to quantify these.

Research Methods

Experiencing a design process that initiated with a contextual step increased my awareness of the many assumptions we make as designers, through which I came to value the core principle of this approach; do not collect data to verify assumptions, but to generate ‘material’ to design with. Relating learning endeavors to the competency areas, I actively applied my competence in Creativity and Aesthetics to further develop my skills in Math, Data & Computing and Technology & Realization. Furthermore, using data as a design material has broadened my understanding of what design materials might be. It left me to wonder what else might be approached this way: How might we use algorithms as a design material? Social structures? And of course, how might we use conversational content as a design material?

Data-Enabled Design

Initiating the process of conducting my own design research processes, I viewed this project as an opportunity to explore directions for my Final Master Project. I buried myself in literature to explore approaches, practices, theory and frameworks and had my mind set on creating the system that could provide live visualizations of perspectives communicated during debates. Enriched by knowledge gained through course materials from Advanced Cognition – the underlying principles of TaCo were already ideated halfway through the process under the name ‘Sotriance’. However, I still lacked the necessary skills to realize such complex systems. In developing these I came to settle with Accessible Abstracts; ideated as a component that would be integrated to the final system with the purpose to verify scientific accuracy of claims that were made in debates. Although ‘Accessible Abstracts’ is framed as the main outcome of this project, the divergent process it was subjected to produced two additional two outcomes; an transparent approach to writing academic works and a framework for ‘socially translucent scientific practices’ (STSP). In terms of the final deliverable, these primarily contributed to the theoretical foundation of ‘Accessible Abstracts’, but, at the same time demonstrate how I apply Creativity & Aesthetics to all facets in my design processes. While I was aware this came with considerable risks at the time, I have learned to be more careful about when and where I should be creative about something through which I further strengthened my competence in Design and Research Processes.

Master Project 2: Accessible Abstracts

Through this course, I became aware of core behavior change theories and learned to apply these to design, further expanding my perspectives in terms of User & Society. Specifically, how designing for societal change can be accomplished through design that intervenes at a micro-level; a user level. Reflecting on how learning outcomes are integrated in my Final Master Project, the aspect of peripheral cues as described by the Elaboration Likelihood model has inspired the idea to provide access to conversational content as a peripheral cue; to enhance the likelihood it would be further deliberated upon. Though doing so means I did not exactly apply the theoretical framework as it was taught during the course, it proves yet again how relating seemingly distant topics makes for interesting outcomes that fits the aesthetic of being a designer.

Design for Behavioral Change

Design for Social Innovation, as prof.dr.ir Hummels does, broadened my perspective on the relationship designers have with User & Society. Once again, the experiences provided through teaching further proved to me how, in physically appearing to one another, a design space is formed by those moving and interacting in it. Moreover, the participatory way of teaching further provided insights developing a system that traces the process rather than only the outcome through which the principles in designing for transformative practices – complexity, situatedness, aesthetics, co-responsibility and co-development – resurfaced in my working memory.

Through the deliverables I was able to further develop the competence in Creativity & Aesthetics and developed a visual aesthetic which embraces complexity similar to how I learned to embrace this in terms of designing for User & Society. I gained perspective in terms of considering cultural background and lived experiences, how these may be defined and why, in all if not most cases, is not up to the designer to do so.

Design for Social Innovation

Adopting the first step in the Data-Enabled Design approach, I initiated my process by collecting data with the aim to generate material for design. Lessons learned in designing with and for machine learning enabled me to configure datasets that would facilitate its application. Validating COCOnUT and learning about topic modelling algorithms further contributed to my aims in getting a grip on AI and provided insights to how qualitative data could be analyzed after being quantified. Furthermore, in creating COCOnUT I managed to I realized the aims I set in reflection for Research Methods by creating a prototype that quantifies qualitative data

Reflecting upon the outcomes, I can safely say my development went far beyond any expectations I had. Considering my disappointed in not conducting proper user tests due to limited time left after developing the necessary skills, I look forward to doing so as soon I start my PhD in August.

Though formally opting for an additional standalone project in which I viewed this project as an opportunity to strengthen my competence in Technology & Realization and Math, Data & Computing, in hindsight, it may have very well been the beginning of my Final Master Project.

Master Project 3: COCOnUT

I continuously pushed myself to find the next challenge and developed a unique profile as a designer, which is characterized by strong social cognition and technological skills to produce fully experiential interactive prototypes of high-fidelity. I have come to learn that being creative means to be open to exploration and interpretation, for which I employ a creative mindset under the motto: “Shoot for the stars, land on the moon”. Meanwhile, aesthetic design – to me – is defined by elegance, efficacy and ease of use. These definitions constitute the foundation through which I have developed my unique profile as a design researcher. Which had a catalyzing effect on my development in the competency areas User & Society, Technology & Realization and Math, Data & Computing, through which already blurred lines between the latter two became non-existent. Integrating creativity towards realization of aesthetic design, research and practice as described, has proven to produce remarkable results in both outcome and personal development. Whether it is analyzing societal problems and collecting perspectives, ideating, conceptualizing, conducting user studies, prototyping, creating a research setup, hard- and software use, enhancing code mathematically, collaborating, communicating, organizing or even writing and dealing with scientific information: there is literally no limit in aspects to consider Creativity & Aesthetics in Design and Research Processes.

In my career as design researcher, I will continue to improve on maintaining a balance between creating an interesting piece of technology, conducting research and documenting the knowledge generated through these. Though I still find it challenging to frame my work as a designer, I possess the knowledge and skills to overcome these challenges.

Reflecting on my overall growth as a designer, I have gown far beyond what I expect when I started the Master. With the design of interactive artefacts under siege by continuous lockdowns, I buried myself in literature and gradually shifted my focus to approaches, practices, theory, frameworks, through which I can confidently say I have become an expert in conducting Design & Research Processes. As software development filled the void left by tangible artefacts, proficiency in programming became a valuable skill that enabled me to design the prototypes I could once only imagine. With a strong basis in coding C/C++ and Java with Arduino and Processing and sufficient knowledge in posting and requesting data from servers through courses provided, I became become proficient in using other editors and integrated development environments like Visual Studio Code, Eclipse and Visual Studio. A process through which I learned to make use of SDKs, cloud infrastructures and a variety of frameworks for development (e.g., React.js, Next.js, Express.js, /psi (C#), Google Console, Azure Cloud Platform/Azure Cognitive Services). Where I could not grasp the concept of machine learning, let alone designing with and for machine learning, I now understand its principles and the mathematical concepts underlying these and feel confident in integrating these in design processes. 