The Human Computer Interaction programme consists of 80 EC (European Credits) of course work, divided over a compulsory and elective part. Below you can find the descriptions of the various courses. 

Compulsory courses (35 EC)

Primary electives (15 EC)

Adaptive interactive systems

This course is about the design and evaluation of interactive systems that automatically adapt to users and their context. It discusses the layered design and evaluation of such systems. It shows how to build models of users, groups and context, and which characteristics may be useful to model (including for example preferences, ability, personality, affect, inter-personal relationships). It shows how adaptation algorithms can be inspired by user studies. It covers standard recommender system techniques such as content-based and collaborative filtering, as well as research topics such as person-to-person recommendation, task-to-person recommendation, and group recommendation. It also discusses explanations for adaptive interactive systems and usability issues (such as transparency, scrutability, trust, effectiveness, efficiency, satisfaction, diversity, serendipity, privacy and ethics). The course content will be presented in the context of various application domains, such as personalized behaviour change interventions, personalized news, and personalized e-commerce.body { font-size: 9pt;

Natural language generation

The taught component of the course will consist of four parts:

I. General Introduction. In the first part of the course you will learn what the different aims of practical and theoretical NLG can be, what are the main elements of the standard NLG pipeline, how NLG systems are built, and how they are evaluated. Template-based and end-to-end systems will be discussed briefly.

II. Practical systems. You will get acquainted with a range of practical applications of NLG; a few will be discussed in detail: candidates applications are medical decision support, knowledge editing, and robo-journalism. Strengths, weaknesses, and opportunities for the practical deployment of these systems will be discussed. If time allows, we will devote attention to multimodal systems, which produce documents in which pictures or diagrams complement a generated text.

III. Module in focus: Referring Expressions Generation. We will zoom in on one part of the standard NLG pipeline, which is responsible for the generation of referring expressions (e.g., as when an NLG system says “the city where you work”, or “the area north of the river Rhine”). We will discuss a range of rule-based algorithms, and some that are based on Machine Learning.

IV. Perspectives on NLG. We will discuss what linguists, philosophers, and other theoreticians have to say about human language production, and how this relates to NLG. We may start with a Gricean approach, and continue with the Bayesian-inspired Rational Speech Acts approach. We will ask how accurate and how explanatory existing NLG algorithms are as models of human language production (i.e., human speaking and writing), and what are the main open questions for research in this area.

The core of the course will be presented in lectures. Additionally, students will be asked to read, present, and discuss some key papers and systems which illustrate the issues listed above.

Mobile interaction

Mobile devices, such as smart phones and tablets, have become as powerful as traditional computers, often replacing them for various tasks. Yet, interacting with them remains challenging due to issues such as limiting form factor, mobile context, etc. On the other hand, it is exactly this form factor, context, and other characteristics of mobiles that provide us with new and exciting opportunities for alternative usages. Examples range from innovative mobile games, to mobile AR (augmented reality) applications. In this course, we will have a closer look at standard interaction with mobiles (e.g., via touch screen; including potential issues as well as opportunities), address new approaches, and look into related current and future research -- including wearable devices (e.g., head mounted displays, such as Google Glass, wristbands and smart watches, such as the Apple Watch). Concrete application domains include mobile gaming and mobile video.

Multimodal interaction

This course covers multimodal (multisensory) perception and interaction.
The course starts with a discussion of the fascinating world of human visual, auditory and tactile perception and the use of its potential in designing novel interfaces for interacting with virtual worlds.
Furthermore, augmented reality is covered as one particular example of multimodal interaction. In the practical part, students will apply the theoretical background of multimodal perception and multisensory input to concrete state-of-the-art examples (e.g., from virtual or augmented reality).

Cognitive Modeling

Formal models of human behavior and cognition that are implemented as computer simulations - cognitive models - play a crucial role in science and industry.

In science, cognitive models formalize psychological theories. This formalization allows one to predict human behavior in novel settings and to tease apart the parameters that are essential for intelligent behavior. Cognitive models are used to study many domains, including learning, decision making, language use, multitasking, and perception and action. The models take many forms including dynamic equation models, neural networks, symbolic models, and Bayesian networks.

In industry, cognitive models predict human behavior in intelligent 'user models'. These user models are used for example for human-like game opponents and intelligent tutoring systems that adaptively change the difficulty of a game or training program to a model of the human's capacities. Similarly, user models are used in the design and evaluation of interfaces: what mistakes are humans likely to make in a task, what information might they overlook on an interface, and what are the best points to interrupt a user (e.g., with an e-mail alert) such that this interruption does not overload them?

To be able to develop, implement, and evaluate cognitive models and user models, you first need to know which techniques and methods are available and what are appropriate (scientific or practical) questions to test with a model. Moreover, you need practical experience in implementing (components of) such models.

In this course you will get an overview of various modeling techniques that are used world-wide and also by researchers in Utrecht (esp. in the department of psychology and the department of linguistics). You will learn their characteristics, strengths and weaknesses, and their theoretical and practical importance. Moreover, you will practice with implementing (components of) such models during lab sessions.

Relationship between goals and examination
The learning goals will be examined in three ways:

  1. Students will implement components of cognitive models in computer simulations during computer practicals. These assignments will be graded.
  2. Students will evaluate the scientific literature by orally presenting and critiquing scientific papers that include cognitive models. The presentation and critiquing will be graded.
  3. Students will be tested on their general knowledge of cognitive models in an exam.

Technologies for learning

In this course you will study advanced software technologies for learning, such as serious games in which you have to develop a sustainable city, simulations such as a virtual company that you have to run, competing against several other virtual companies, intelligent tutoring systems for learning mathematics, physics, or logic, etc. In particular, you will study the underlying intelligence necessary to determine what a student has learned, what a student should do next, give feedback to a student, etc.In this course you will learn about the use of software technology to support student learning.
Student learning is supported by applications such as:

  • Serious games
  • Simulations
  • Intelligent Tutoring Systems
  • Exercise Environments
  • Automatic Assessment Systems

These applications use technologies such as:

  • Model tracing: does a student follow a desirable path towards a solution?
  • Static and (sometimes) dynamic analysis: what is the quality of a student solution?
  • Learning analytics: what do students do in a learning application?
  • User modeling: what does a student know?

which build upon:

  • Strategies, parsing and rewriting
  • Bayesian networks
  • Datamining
  • Constraint solving
  • Artificial Intelligence
  • Domain-specific technologies, such as compiler technology for the domain of programming.

Sound and music technology

Sound and music provide powerful ways for impacting the human experience involved in the engagement with games and media. In this course, you will learn how to apply and develop computational methods to extract, process and utilize music information from digital sound and music in the context of newly emerging research areas within games and media. You will learn how sound and music information is crucial for the human experience, and how the computational modelling of sound and music contributes to the enrichment of this experience in games and media. This encompasses that you will get to know both basic concepts on how human listeners extract, make sense of and give meaning to information from sound and music, and how these basic concepts are used, researched and applied through computational technology.The course is structured around three main modules:
A: Sound and music for games
B: Analysis, classification, and retrieval of sound and music for media
C: Generation and manipulation of sound and music for games and media
The course will cover key topics for sound and music technology in the context of games and media, such as interactivity and immersion in games through sound and music (A), classification and retrieval of similar musical objects in multimedia (B), and the utilization of the emotional and affective qualities of music in games and media (A, C). You will learn what specific technologies are developed and required within these key topics, such as automatic pattern discovery, sound separation, voice separation, automatic segmentation, and feature extraction and manipulation (B). For studying, discussing and employing these technologies you will get to know different representation forms of music information in audio and symbolic data (A), different musical dimensions such as melody, rhythm, harmony, timbre and loudness (A, B), and how they are modelled through computational features (A, B, C). Moreover, you will learn about different general strategies for developing computational models for sound and music processing, such as model-based versus data-driven approaches, and about the challenges of evaluating these models.

Multimedia discourse interaction

There is no content available for this course.

Secondary electives (30 EC)

ICT advisory

The advisory discipline is an established industry and employs hundreds of thousands of people. Advisory is best described as “creating value for organizations, through the application of knowledge, techniques and assets, to improve business performance. This is achieved by through the rendering of objective advice and/or the implementation of business solutions” (Markham & O’Mahoney, 2013). Giving advice is not limited to a particular industry and can be found in any industry and on many different topics such as taxes, business strategy, marketing, ICT etc. Logically, the focus of this course is on giving ICT advice but to a variety of industries.
In this course we address ICT advisory from four different perspectives: Descriptive, Practitioner, Critical, and Career perspective. These will be addressed in the lectures of the course and are based on the book that is prescribed for this course. Besides the theory you will be practicing your consultancy skills in the skills workshops. Skills include for example presenting, analyzing and writing. Each of the workshops will be provided by a different consultancy company that is based in the Netherlands and concerns a mix of small, medium and large consultancy organizations. Finally you will practice skills and theory in a project where you have to advise a real client. In this project you will work in teams of three students, where the client that you will be working for is provided by one of the consultancy companies. During the project you will produce a number of intermediate deliverables and the end deliverables are an advisory report and a presentation. The deliverables will be graded and determine your grade for the course.
Several consultancy companies will be participating in this course by providing guest lectures, skills workshops and projects at their clients. At the same time you also learn more about the different types of consultancies as we have a nice mix of small, medium and large consultancy companies that participate.During the course you will be undertaking a consultancy project at a client (i.e. a company or governmental institution). The consultancy companies that participate in this course will provide a number of projects at their clients. But we probably need more project so also lookout actively for clients yourself.
Later in the course you might be asked to sign a Non-Disclosure Agreement (NDA) in which you declare that you will handle in the best interest of the client and will not disclose any information you get from the client.

ICT entrepreneurship

A software product is defined as a packaged configuration of software components or a software-based service with auxiliary materials, which is released for and traded in a specific market.
In this course the creation, production and organization of product software will be discussed and elaborated in depth:

  • Requirements management: prioritization for releases, tracing en tracking, scope management
  • Architecture and design: variability, product architectures, internationalization, platforms, localization and customization
  • Development methods: prototyping, realization and maintenance, testing, configuration management, delivery; development teams
  • Knowledge management: web-based knowledge infrastructures,
  • Protection of intellectual property: NDA, Software Patents
  • Organization of a product software company: business functions, financing, venture capital, partnering, business plan, product/service trade-off, diversification

This course is explicitly meant for students Information Science and Computer Science. Pre-arranged or mixed teams are are no problem, it is the product idea that matters.

The aim of this course is to create a prototype and business plan for a novel software product. Students can join the course either with a product idea or without. In both cases your participation in the course must be formally approved.

For the Secondary electives students can also choose from remaining primary electives, courses of the Master's programmes Artificial Intelligence, Computing Science, Game and Media Technology, Business Informatics, other Master's courses within or outside UU (permission required), or one or two deficiency courses (permission required).

Study plan

During the first weeks in the Master's programme all students need to create a study plan. It is expected that you familiarise yourself with the programme to get the most out of it. The study plan is an indicative plan for how you want to go through the programme, but is not binding and leaves you free to change course during the programme.