Workshops

Workshop leaders: Joost Jansen in de Wal & Thijmen van Alphen – University of Amsterdam –

PhD candidates experience the final phase of their project in different ways. The end is in sight. For some it is a relief or a culmination, for others it is a reality check. In any case, completing a dissertation often takes pressure. After all, it has to be finished (but research is never finished!). Also, the search for a follow-up job often starts to take concrete shape in the final year of your PhD. Besides completing the dissertation itself, defending it is something that PhD students may experience differently. For some it is a holiday equivalent to a wedding day, for others it is a formality or is experienced as hazing. The defence is best when you can stand up for your thesis with verve, in front of your colleagues, family and friends, and have an inspired conversation about it with your committee. But how do you create air and space within yourself for this? In this workshop, we will discuss how to arrange the final phase of your PhD in a pleasant way. We will address questions such as: How do you plan this phase? What is a good way to look at (the writing of) your introduction and discussion chapter? What can you consider when putting together a (reading) committee? What expectations are best to go into your defence with? And how do you manage your supervisors during the final stage? Of course, there is also plenty of room for discussing your own input.

Workshop leader: Tuba Stouthart – Eindhoven University of Technology –

     Q methodology enables researchers to study a person’s viewpoints, opinions, beliefs, attitudes, and the like (Brown, 1996). It has been recognized as an emerging method in educational research for investigating subjectivity (Lundberg et al., 2020). By combining both qualitative and quantitative research techniques, Q methodology clusters participants based on their standpoints rather than seeking a statistical representation of the target group. This method explores how participants’ perspectives on the studied phenomenon differ or align. In this sense, Q methodology has been described as the most well-developed method for investigating human subjectivity (Dryzek & Holmes, 2002, p.20).

Recently, Q methodology has been used in several studies in educational research (e.g., Chaaban et al., 2023; Stouthart et al., 2025; Yang, 2023). While most of these studies focused on examining participants’ views, Lundberg (2022) demonstrated how Q methodology can also be applied as a participatory research approach, highlighting its potential as a tool for reflection.

Aim of the Workshop

     The aim of this workshop is to introduce participants to the Q methodology as an effective means of capturing human subjectivity. Participants will have the opportunity to experience how we applied Q methodology in a previous study, including the data collection and analysis processes. We believe that allowing workshop participants to engage with the methodology in a manner similar to that of study participants will effectively highlight the methodology’s potential and facilitate meaningful discussions on its applications in science education research, and practice.

Interactivity during the Workshop

     During the workshop, participants will be introduced to a Q set we developed and used in a previous empirical study. They will be provided with a grid used in Q methodology studies, along with a paper version of the Q set, which consists of 36 statements. The workshop participants will then have time to sort and rank the statements in the grid, allowing them to experience Q methodology from the perspective of the original study participants.

Once the Q sort activity is completed, we will present the semi-structured interview questions used in the study and walk through the data analysis process using the data collected from our original participants. Workshop participants will receive a worksheet to follow along as we present the analysis steps. The participants will be given the opportunity to download the dedicated software KADE onto their laptops and actively participate in the analysis process. Whether participants engage by following the steps on their laptops or by observing, this segment will give them a hands-on experience of Q methodology from a researcher’s perspective.

Reflective Discussions

Towards the end of the workshop, we aim to initiate a discussion among the participants regarding the use of Q methodology in education. We plan to address the following questions during this discussion, which may take place in small groups, depending on the number of participants:

• In what ways does Q methodology have the potential to serve as an effective way for data collection and analysis in educational research, and practice?
• What methodological opportunities and challenges may arise when using Q methodology?

We hope to gather participants’ reflections on these questions using Padlet, where they can also reflect on their experiences in the workshop and the application of Q methodology.

References

Brown, S. R. (1996). Q Methodology and qualitative research. Qualitative Health Research, 6(4), 561–567. https://doi.org/10.1177/104973239600600408

Chaaban, Y., Alkhateeb, H., Abu-Tineh, A., & Romanowski, M. (2023). Exploring teachers’ perspectives on career development: Q methodology research. Teaching and Teacher Education, 122. https://doi.org/10.1016/j.tate.2022.103987

Dryzek, J. S. ., & Holmes, Leslie. (2002). Post-communist democratization: Political discourses across thirteen countries. Cambridge University Press.

Lundberg, A. (2022). Academics’ perspectives on good teaching practice in Switzerland’s higher education landscape. International Journal of Educational Research Open, 3, 100202. https://doi.org/10.1016/J.IJEDRO.2022.100202

Lundberg, A., de Leeuw, R., & Aliani, R. (2020). Using Q methodology: Sorting out subjectivity in educational research. Educational Research Review, 31. https://doi.org/10.1016/j.edurev.2020.100361

Stouthart, T., Bayram, D., & van der Veen, J. (2025). Science Teachers’ Views on Student Competences in Education for Sustainable Development. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.22021

Yang, X. (2023). Creating learning personas for collaborative learning in higher education: A Q methodology approach. International Journal of Educational Research Open, 4, 100250. https://doi.org/10.1016/J.IJEDRO.2023.100250

Workshop leader: Brechtje van Zeijts – Utrecht University

During this workshop we will shortly discuss the merits and pitfalls of The Open Science movement and how this movement relates to the educational sciences. Subsequently, we will explain why preregistration of your empirical studies could be an important step in increasing the accountability but also the validity of your research.  We will provide you with some examples of how to register a study, varying from experimental studies to qualitative studies, and you will also get some hands-on practice in preregistration.

Workshop leader: Inge Molenaar – Radboud University

In her introduction, Inge Molenaar will explore the role of artificial intelligence in education, emphasising the potential for hybrid human-AI collaboration. At the heart of effective education is the goal of fostering student learning and talent development. To achieve this, it is essential to align theories and scientific insights on learning and teaching with the possibilities offered by AI.

Inge will highlight the dual role of AI in education, as a tool and an actor. AI as a tool is used for understanding learners’ learning processes and refining learning theories. AI, as an actor, supports learners during learning and helps teachers improve their teaching. A significant gap exists in the application of theories and scientific insights in AI-empowered educational technologies. Only 50% of learner-facing solutions and 33% of teacher-facing solutions are grounded in these theories, indicating substantial room for improvement.

In her talk, Inge will outline how to design AI in education to connect learning theories and scientific insights with the possibilities of AI. She will contrast the replacement and augmentation perspectives and address the consequences of AI automation on teacher and learner autonomy. Through compelling examples, she will illustrate how AI in education can lead to the replacement, complementation, and augmentation of teachers and learners.

Moreover, she will propose an innovative approach to investigate the complex interplay among AI, teachers, and students, providing a practical framework for integrating AI’s dual role with the established scientific understanding of learning and teaching. This alignment may help to cultivate responsible educational practices that empower students to realise their full potential.

Interactive component

After this introduction, participants will further explore responsible use of AI in education using the common language of the National Education Lab AI (NOLAI). The common language is shortly introduced and related to our co-creation and co-implementation programs, in which we develop novel AI applications together with schools, scientists and companies.

We will see how co-creation projects are designed and developed with the help of these models. Participants will learn how to describe AI solutions in our common language and in this light discuss possible futures of AI in education and the consequences thereof.

 Our common language contains four main models:

1. The Detect-Diagnose-Act model helps understand how AI is functioning. It describes the input data (detect), the constructs being interpreted (diagnose) and how the AI is acting (act).
2. The adaptivity model outlines 3 approaches to personalization, namely step, task and curriculum adaptivity.
3. The 6 layers of the automation model describes control division between the teacher and the AI. This indicates how AI automation and teacher autonomy are related in particular applications.
4. The human-AI collaboration framework that distinguishes task level changes due to the use of AI applications, conceptualized as replace, complement and augment.

References

• Molenaar, I. (2021). Personalization of learning: Towards hybrid human-AI learning technologies. In OECD digital education outlook 2021: Pushing the frontiers with AI, blockchain, and robots (pp. 57-77). OECD Publishing
  https://doi.org/10.1787/589b283f-en
• Molenaar, I. (2022). Towards hybrid human-AI learning technologies,
  European Journal of Education. https://doi.org/10.1111/ejed.12527
• Molenaar, I., Horvers, A. & Snoeijen, K. (Ed.). (2024). Magazine AI in onderwijs: Samen werken aan slimme technologie: Wat weten we, wat willen we, wat kunnen we? Nijmegen: Nationaal OnderwijsLab AI (NOLAI), see English version onine
• https://www.ru.nl/nolai/over-nolai/magazine
• https://www.ru.nl/over-ons/nieuws/van-theorie-naar-impact-publicaties-die-ertoe-doen

Bio Molenaar

Inge Molenaar is the Director of the National Education Lab AI (NOLAI) and a Professor of Education and Artificial Intelligence at the Behavioural Science Institute, Radboud University, Netherlands. With over 20 years of experience in technology-enhanced learning, she has successfully navigated roles ranging from entrepreneur to academic.

Dr. Molenaar’s research focuses on innovative, technology-empowered approaches to optimize human learning and teaching. Central to her work is the application of data, learning analytics, and artificial intelligence to understand the dynamics of learning over time.

Envisioning Hybrid Human-AI Learning Technologies, Dr. Molenaar aims to augment human intelligence with artificial intelligence, empowering both learners and educators to make education more efficient, effective, and responsive to individual needs. Her research group, the Adaptive Learning Lab, investigates how self-regulated learning can be supported through technology. At the National Education Lab AI, she develops new educational practices using AI and explores the responsible use of AI in education.

She is a recipient of several prestigious grants, including an ERC Starting Grant and funding for the National AI and Education Lab (NOLAI) and holds Master’s degrees in Cognitive Psychology and International Business Studies, as well as a PhD in Educational Sciences from the University of Amsterdam.

More information will be available soon