During the programme

Digital technology transforms society on a fundamental level and generates data on an unprecedented scale. Advancements in machine learning and artificial intelligence allow us to transform these large amounts of data into valuable information. This course introduces important theories and concepts that help with explaining what role data and digital technologies have in modern society. It critically investigates the historical developments that led to the current age of big data, artificial intelligence, and increasing digitalisation. In order to assess  why digital technology has become so ubiquitous and how it changes the way societies are built up, we need to understand crucial concepts first:

• datafication
• digital transformation
• digital cultures
• aritificial intelligences and machine learning,
• digital identity
• data ecologies and data-driven feedback loops
• data literacy and digital ethics
• value sensitive design

Future data professionals and designers create the technological solutions that make these concepts manifest and so influential on our social environment; they have profound political, economic, and cultural implications. All of this raises important questions about the role of digital ethics and data literacy to build a digital society that is fair, inclusive, and sustainable. What can we learn from past mistakes? How can we try to avoid future disasters?

Digital communication technologies have penetrated many of our daily activities. Reading the newspaper, controlling household appliances, shopping for a pair of jeans, managing bank affairs, communicating with friends and family, monitoring our health, or going on a date. All of these activities, small and large, have been mediatized and digitized due to the digitalization of society. As a result, there is a growing need to unravel the influence of media and (communication) technology on individuals, groups and society as a whole. 

Media psychology is a burgeoning field, focusing on the psychology behind media and (communication) technology use and impact. Hence, it is also referred to as the “interface between media and the human response” (and in that sense a preparation for block B: Designing for Interaction, which specifically focuses on interfaces). In New Media Psychology, you will learn to critically assess human interaction with media and (communication) technology, by using a psychological lens. Such a lens is essential in the preparation for your various job roles in various industries that involve mediated communications and information technologies, including but not limited to: media producers, interaction designers, (UX-)researchers, public relations and advertising specialists, content strategist but also health providers, educators and policy makers. 

In the science fiction novel 2001: A Space Odyssey, a group of man-apes encounters a monolith, which grants them the ability to use tools and weaponry for hunting and fighting. These newly found abilities dramatically shifted human evolution. The encountered monolith was one of many throughout the galaxy and represented machines built by an unseen extraterrestrial species. Metaphorically, you, as a practitioner with a background in design, media, communication, or journalism, are encountering more and more of these “new machines”. These new machines provide you the opportunity to make use of new data science techniques in order to change the world. Many of these data science techniques, however, are developed by computer scientists and seem daunting and less accessible for you as a practitioner. This is why this course was designed. To help you unlock the power of data through code. 

This course advocates that code literacy is essential to overcome limitations and open up black boxes. Code is a crucial requirement to participate in this new digital world. Even without choosing to become a professional or being a full-time software developer, understanding how code affects (almost) everything around us nowadays is vital! Everyone can push a button in Power BI, but not everyone understands the intricacies of code. Coding teaches us to think in different ways, to identify, break down, and solve problems, to re-use things that others have made, and integrate them into your own creative process.

The user interface is at the heart of all data-driven concepts. While interacting with an interface, the user creates interaction data. This interaction data can be direct, e.g., filling in a form or choosing a movie and indirect, e.g., the location of the user or the type of device. These interactions are reciprocal: the user generates data and the data-driven concept in turn communicates with the user. Recommendation systems can be used to illustrate this concept. An interface can suggest which movie to watch, what newspaper article to read, what product to buy or how a game adjusts the level of difficulty. These are all examples of reciprocal interfaces. However, in order to be able to do so, the data-driven concept has to learn from the many user interactions in order to become more valuable over time.

The Fundamentals of Machine Learning course focuses on learning from the constant feedback loops between human and machine. We will employ traditional statistics and machine learning to analyse these feedback loops, i.e. machine learning.

The process of digitization fundamentally changes the heart of any organization, whether it is a city, a government or a company. Advanced technologies facilitate processes like crowdsourcing, in which huge user-crowds are transformed into an organization’s collaborative workforce, blurring the line between employee and customer. Next to that, technologies like Blockchain allow us to build decentralized networks that facilitate and capture any kind of (value) exchange and that, via smart contracts, secure agreements and establish trust. It is the combination of these kinds of strategies and technologies that gradually will lead to new kinds of Decentralized Autonomous Organizations (DAO’s). 

In this course, you will learn how the right allocation of creativity, value, and technology may strengthen core processes. If these core processes are indeed well chosen, the combination of almost unlimited computation and (user) interaction may result in organizations and networks that scale.

This course is designed to prepare students for their graduation. During graduation, students have to develop a working prototype of a data-driven concept, and an academic paper that could be submitted to a conference and/or journal. The prototype can be used as a proof of concept for the research or as a tool to aid the research process. In any case, the prototype and academic paper are closely intertwined. 

To prepare the students for this challenging task, they will write a convincing and feasible graduation proposal (i.e., a road map to guide students during the final stage of their education). The course Creative Research supports the writing process, and includes sparring on concept development and scoping, support in scholarly research (academic reading, writing, literature search, APA), methodology, research planning and analytical thinking. In essence, the course focuses on writing a preliminary version of the academic paper. 

In 'State of the Art Technology', students are challenged to explore new technological innovations and learn how to master them for their research quickly. Through the use of critical thinking, play, experimentation, and hands-on making practices, students research a problem space of their interest, explore the possibilities offered by the technology of their choice, and expose its limits.
The course encourages participants to forego procedural design modes in favour of critical making. Students become creative scientists and autonomous critical makers. Rather than create prototypes to validate ideas of products, students develop prototypes as an instrument to initiate a critical and social reflection on technologies.

Examples of state of the art technologies include (but are not limited to) deep learning algorithms (image recognition, natural language processing such as GPT-3, voice processing), voice-controlled interfaces, smart cities / IoT and quantum computing.

In the graduation project, students show that they have become confident discourse participants with a sound academic understanding of datafication and the digital transformation but also practice-focused designers of data-driven solutions.

The learning goals include: (1) the ability to research, analyze and understand the use of data in specific contexts of use; (2) to develop data-driven concepts that fit the research problem and are potentially scalable for value-creation; (3) to develop and test a data-driven prototype built in code with an engaging interface; (4) to critically evaluate the development process and the potential impact of the proposed prototype on the domain in focus; (5) and to communicate this clearly to diverse audiences of relevant stakeholders. 

The graduation phase hence unites the three tracks of conceptual, human, and tech in one project.