Doctoral Education at IDE

The IDE Graduate School has developed her own PhD course in the 'Discipline' related skills area, the IDE Research Course, which is mandatory for all IDE PhD candidates. More information on this course is available at the IDE Research Course page.

About 4 times a year the IDE PhD Council organises a colloquium to give PhD candidates of the IDE faculty the opportunity to share their work and get to know each other. The aim is to have 2 presenters from each department who will present an overall impression of their work or a more specific project he/she is currently working on. The presentations take 5-10 minutes and are followed by a 5-minute Q&A. For presenters it’s a great opportunity to practice their presentation, to get some feedback, or to start some nice discussions with your peers. For attendees it’s a nice opportunity to meet their peers, learn about the interesting work they are doing and to participate in interesting discussions.

Presenters can obtain 1 GS credit for ‘addressing a small audience’ (learning on the job activity) and attendees can obtain 0.5 GS credits per two colloquia (max. 2.5 GS credits). Please use this form to register your attendance and to claim the GS credits.

PhD Day is an annual day organized by the IDE PhD Council for all IDE PhD candidates. Participants connect, network and share research interests. The day is filled with activities like workshops, talks and group sessions, and is a great opportunity for PhD candidates to get to know each other and their field of work. Participants can obtain 1 GS Credit once. In 2023 the PhD day took place on the 10th of May.

Course coordinator: Wilfred van der Vegte
Course runs: Q4, once per year
Course sessions: 4 half-day sessions, each about one week apart
Preparation and homework: 14-20 hours
Contact/enrolment: w.f.vandervegte@tudelft.nl
Number of participants: 5 - 15
Credits: 3 GS credits

COURSE REQUIREMENTS
In order to participate in this course, you must:

• ideally be in your second year or first half of your third year
• have a laptop computer available on which you have rights to install software
• ideally have some research data that lends itself for exploration as presented in the content description. You may be able to identify such a dataset during the course.

DESCRIPTION OF THE CONTENT
From experiments, user observations, surveys, corpora of literature, etc., data can be extracted that can be analysed to reveal patterns and relationships. For researchers, these can provide insights to generate or to test hypotheses. In the context of design, the increasing connectedness and digitalisation of products and services also increases the availability of data that can provide insights to researchers in the field.
Statistics, visualizations, machine learning and text mining are powerful enablers for discovering these insights, but they can seldom work with the available data straight away. Typically, a succession of steps is needed to reorganize the data and extract relevant elements. Until recently, this composition of processing workflows, or pipelines, has been a tedious process that could best be left to specialists fluent in programming languages such as Python and R. However, today’s visually oriented toolkits (e.g., RapidMiner, KNIME, Orange) can conveniently replace much of the traditional text- based coding needed for setting up complete data-processing workflows. These toolkits also facilitate communication and explanation of the deployed workflows to involved stakeholders, and produce models revealing meaningful relationships within the data. In the course we will use visual programming to explore rich datasets for insightful patterns and relations with the goal to generate actionable knowledge for IDE research. The exploration is built upon selected fundamentals of data processing and analytics based on the needs of the participants.

LEARNING OBJECTIVES
Having completed this course, you will be able to select and apply processing steps to explore rich datasets related to IDE research, and to convert opportunities for finding meaningful relationships and patterns into executable processing workflows using visually oriented analytics tools.
More specifically, you will:

1. understand the typical types of relations that can be revealed through basic statistics (mean, max, min, mode, etc.), visualization, machine learning (ML) and text mining (TM);
2. have understanding of the main differences and similarities between statistics and ML;
3. understand and apply the key preparation steps to make raw data suitable for visualization, basic stats, ML and TM - such as merging data sources, data smoothing, binning, pivoting and aggregation;
4. understand the distinction between models explaining the relations mentioned under (1) and algorithms to find the best fits for parameters in a model. Also, you can select explainable models if applicable, and interpret such models;
5. be able to apply data visualizations in an explorative fashion to help identify appropriate preparation steps.
6. be able to implement the above actions to create data-processing workflows using a visually oriented tool, and extract meaningful knowledge in a way that clearly exposes how it contributes to scientific progress or can beimplemented in a design context.

TEACHING METHODS
The course follows an active-learning approach, in which a balanced composition of classroom presentations and discussions, tutorials, project-based learning, and self- study is applied.

TEACHING MATERIALS
Software: Orange datamining software for Windows, Mac OS or Linux. Free download from https://orangedatamining.com/
Other teaching materials will be proposed during the course sessions.

ASSESSMENT
You will receive your certificate based on active participation in the course, which is evidenced by taking part in classroom and online discussions and showing evidence of having gained hands-on experience in data processing and analytics.

Course coordinator: prof. dr. P.J. Stappers
Course runs: Once a year (to be announced)
Course sessions: 4 half-day in-person sessions
Preparation and homework: 14-20 hours
Contact/enrolment: p.j.stappers@tudelft.nl
Number of participants: 5-15
Credits: 3 GS Credits

COURSE REQUIREMENTS
In order to participate in this course, you must:

• ideally be in your second year or first half of your third year
• use design actions as part of your research method (probably you have a design background in your master)

DESCRIPTION OF THE CONTENT
Design is young as an academic discipline, and its research methods are diverse, ‘borrowing’ from natural and social sciences, humanities, engineering, and design itself. Increasingly, PhD researchers with a design background attempt to make their design skills part of the method of research. This field of ‘Research through Design’ (RtD) confronts the PhD researcher with having to explain how design actions contribute to knowledge generation, what types of knowledge are delivered, and how to conduct these studies with dependable quality.
This course follows a review chapter [1] giving sample RtD projects, and themes in the academic literature (find it by typing ‘research through design’ into a plain Google search). You will read some of the key papers, and discuss how your own work relates to the themes from the literature.

LEARNING OBJECTIVES
Having completed this course, you will be better able to

1. position your own project in the RtD ‘spectrum’
2. argue for the types of results (knowledge, impact) of your work
3. argue in what ways your design skills are part of the research
4. understand the position of design research in between other disciplines

TEACHING METHODS
The course consists of four meetings of half a day. In preparation you will read the chapter and some key papers, reflect on your own project, and present and discuss about these themes in sessions with peers, moderated by the course coordinator.

TEACHING MATERIALS
The backbone of the course is the chapter [1], which is available online. A printed version is also available for participants. Also some other papers of required and optional reading will be made available. These will be determined during the course on the basis of the ongoing discussions.

ASSESSMENT
You will receive your certificate based on active participation in the course, which is evidenced by taking part in classroom presentations and discussions.
Reference

1. Stappers, P. J., & Giaccardi, E. (2017). Research through Design. In M. Soegaard, & R. Friis-Dam (Eds.), The Encyclopedia of Human-Computer Interaction. (2nd ed., pp. 1-94). The Interaction Design Foundation. Chapter 43.

Course coordinator: prof. dr. Bregje van Eekelen
Reading group initiators: Abhigyan Singh, Boudewijn Boon, Ehsan Baha, Peter Kun
Course runs: year-round, every 3-4 weeks
Contact/registration: dr. Abhigyan Sing (A.Singh@tudelft.nl)
Credits: 1 GS Credit for every two sessions attended (max. 5 GS credits)

DESCRIPTION OF CONTENT

Aim: The purpose of the Reading Seminar is to develop an intellectual dialogue within IDE by discussing and reflecting on design and socio-cultural theories.

Approach: The Reading Seminar meets every 3-4 weeks. Participants prepare in advance by reading and reflecting on pre-announced texts for the seminar. On occasion, we will invite foreign experts (authors of the selected text) to discuss their works with us. Each seminar lasts around one hour and forty-five minutes. Participants of the seminar can also propose a topic and texts for upcoming seminars. (Please note that the reading seminar will not consist of any lecture).

Requirement: The only requirement for participation in a Reading Seminar is to come prepared by having read and reflected upon the announced texts and to actively participate. An interest and desire to ask and engage difficult questions during the seminar will be much appreciated.

DELIVERABLES
N/A

Course coordinator: dr. Rick Schifferstein
Course runs: Once every 2 years (2022, 2024, ...), for 5-6 months, depending on expressed interest. Usually starting September/October
Contact: Before start of the course (will be announced in PhD mailing list)
Credits: 5 GS Credits

DESCRIPTION OF CONTENT
Writing a paper for an academic journal is an art in itself that asks for a particular style that can only be learned through extensive practice. Writing a paper can be a cumbersome and energy-draining activity: Every detail needs to be checked, every sentence needs to fit in, and the wording should be precise, the line of argumentation should be solid, statistical analyses should be appropriate and well executed, every conclusion should be substantiated by the study results. In addition, every journal seems to have their own rules, some of which are implicit. Therefore, many researchers struggle to complete their papers.
In this Masterclass, Rick Schifferstein provides a stable basic framework for writing academic papers, using the draft papers, reviewer reports, and rebuttals from his own publication practice as exemplary materials. Participants will receive a detailed insight in his personal approach to academic writing, supplemented by many practical suggestions and personal writing tips. The class helps both in speeding up the writing process and in improving the quality of the scientific contribution.
The meetings provide a ‘training on the job’ approach: participants evaluate and discuss each other’s work and develop a writing strategy for the next part of the paper. In between meetings, participants revise their
contribution and work on the subsequent section for their paper. When they keep up with the schedule and finish the entire course, they will have finished a paper that is ready for submission. The scheduled joint meetings really help to motivate participants to continue to work on their papers over the course of the meetings.
This Masterclass is intended for academic staff and experienced PhDs who are in their 2nd to 4th year who already have basic writing skills from courses on scientific writing and writing papers for conferences. The course is tailored towards participants who have finished an empirical study in the field of human experience, consumer behavior or design research, and would like to transform their outcomes into a high-quality journal paper for an academic journal (e.g., International Journal of Design, Materials & Design, Applied Ergonomics, Design Studies, Journal of Engineering Design, Journal of Design Research, CoDesign, Journal of Consumer Research).

DELIVERABLES
For every meeting (except the first one), students write a section of their paper. During the meetings, students give each other peer formative feedback on the assignment. Students who cannot attend a meeting are requested to team up with another student, in order to exchange mutual personal feedback on the writing assignment for that meeting. After the course, students hand in a full paper in which all peer comments have been addressed, and that is ready for submission in a journal. The course coordinator will determine a final grade on the basis of this paper. If sufficient, the student will get a certificate.

Linked course: Advanced Embodiment Design (ID4175)
Course coordinator: dr. Haian Xue (h.xue@tudelft.nl)
Course runs: 3rd/4th period (starts February)
Contact the coordinator: as soon as possible
Credits: 3 GS-Credits

DESCRIPTION OF CONTENT
Designing for experience requires three basic design skills: 1) understanding the fundamental differences among different types of product experiences (i.e., design for aesthetics, meaning, and emotions); 2) identifying the level of human-product interactions for facilitating a specific experience (i.e., micro, macro, and metal levels); and 3) figuring out which product property is the best carrier for the desired human-product interaction (i.e., shape/ sound/materials; product function; context of use and interaction).
By following this module, PhD candidates will gain both theoretical and practical skills into developing product experiences. By the end of the course, with the help of a ‘product experience matrix’, PhD students will be able to analyze the properties of a current product and conceptualize a future interaction scenario for eliciting a desired experience.
The content of the exercise depends on a PhD candidate’s interest in a particular product experience.

DELIVERABLES

• One essay
• Two Product Experience Matrices

The course runs in the first block of Spring semester (Q3). An essay on current and future product experiences is required together with two Product Experience Matrices (one for current and one for future product experiences). PhD candidates will participate in three product experience workshops (aesthetics, meaning, and emotion) through which they can also test their drawing or building skills. Feedback is given by experts at the workshops and/or through one-on-one scheduled meetings.