The Learning Technology & Analytics research group investigates the use of technology in education and study data to improve equal opportunities for students.

About the research group

Study data and technology in education can evoke strong emotions. Some have the image that it will mainly monitor and select students and that this might have a negative impact on them: “Computer says no”. Meanwhile, on the contrary, others are enthusiastic about the potential of data and technology to advance education and students. Both images have a grain of truth, as there is opportunity in both directions. One thing is sure: technology and data already play a crucial role in innovations in education, and – given the rapid pace of technological development – this will only get stronger. With that, the need for research into this topic is growing too.

The research group has two research themes: Learning Technology & Learning Analytics

Within the Learning Technology theme, we use experiments to investigate the interaction between technology, students, and teachers in education. How is technology used, what does it bring, and what does it take away? What makes students, teachers or policymakers not adopt technology in education? What is the potential impact of advanced technologies – such as AI, gaming, virtual or augmented reality – on student teaching and supervision?

In the second theme, Learning Analytics, we examine study data generated in education or its context: data from education systems, education administrations, and public data. What are the statistical relationships in these data? How equal, diverse, and unique are our students within different programmes? We examine not only the data but also the usefulness of insights. What can these analyses mean for teaching, guidance, and education policy? How do educational institutions’ teachers, students, and support staff use data to make decisions?

A particular focus in the second theme is research on students who are – proportionally – underrepresented. What biases exist in study data, and how can we eliminate them? What might education and guidance look like, then? What are the opportunities of different students, and how equal are they? What can we do to improve the equality of opportunities?

Lines of research

A new senior researcher is developing the lines of research in the Learning Technology theme. The Learning Analytics theme of lectorate has four lines of research:

  1. Learning Analytics: this line focuses on analysing data affecting the quality of learning and teaching. These insights can help to design teaching, learning processes, and study ability.
  2. Student Analytics: by analysing data at the group level, we can contribute to the quality of programmes with insights into programme design and students’ study paths.
  3. Institutional Analytics: insights from this line of research can help to set policy and strategy for the entire university, as well as contribute to national, European, and international research.
  4. Inclusion Analytics: study progression and success of students with support needs is a line of research in which we aim to be meaningful at all levels, as mentioned earlier.

Within each line of research, we focus on a) the selection of relevant study data, b) methods of analysis, c) practical application of solutions, degree of adoption, and impact measurement, and d) communication and dissemination of knowledge.

Theo Bakker

About the professor

dr. Theo Bakker

Photographer: David Meulenbeld

Theo Bakker has a broad background. He studied Theology, Informatics, and Management Consulting. He obtained his PhD in Clinical Developmental Psychology on the study progress and study success of students with autism.

Since 2004 study data have been the thread running through Theo’s career. Initially with implementing educational administrations and later using the information from these systems in the context of study success. He joined the Vrije Universiteit Amsterdam (VU) in 2014 via the University of Amsterdam and Deloitte Consulting. In 2022, he obtained his PhD for his thesis’ Study Progression and Success of Autistic Students in Higher Education, A Longitudinal, Propensity Score-Weighted Population Study’.

In 2021, Theo started at The Hague University of Applied Sciences as director of the Education, Knowledge & Communication department. After his PhD, he combines all his knowledge and experience as a Learning Technology & Analytics professor. In addition, Theo works as a university lecturer at the Faculty of Behavioural and Movement Sciences of the VU.

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Team Onderzoek


Within our research group, researchers, lecturer-researchers and students work closely with practice partners to conduct research. Together they are committed to translating the results of the research -knowledge and insights- into the practice of education and the region.

Meet our team

Equal opportunities in higher education call for consideration of exceptions



Nuffic, THUAS, and VU investigate Studying without Borders

Three institutions investigate student mobility and the labor market success of graduates in higher education.
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No Fairness without Awareness

Analysing intake, progression and outflow is essential for universities of applied sciences to gain insight into the position and significance of the school for students and the region.
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Study data at The Hague University of Applied Sciences

The Learning Technology & Analytics lectorate analyses study data and provide insights to improve policy, quality, and education. Which analyses are helpful to make? And how to do so safely?
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18 June 2024

Nuffic, THUAS and VU investigate Studying without Borders


From each of their unique perspectives, the three institutions investigate international student mobility and the labor…
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3 June 2024

Npuls Vision process launched: share your input on AI and equity in education


The applications of AI and study data in education are taking off, but the question is whether this reduces or…
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6 February 2024

Winning prizes with Data Analytics


Two teams of THUAS were able to take a shared second prize completing the 'Leadership Challenge with Data…
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Predicting Academic Succes of Autistic Students in Higher Education

Little is known about early predictors of academic success for young people with autism pursuing university studies.


Study Progression and Success of Autistic Students, A Longitudinal Propensity Score-Weighted Population Study

Theo investigated the study progression and success of students with autism in higher education by statistically comparing their background and study progression with students with or without disabilities.

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Study Progression and Degree Completion of Autistic Students in Higher Educaion: A Longitudinal Study

Article published in The International Journal of Higher Education Research on PhD on the study progression and degree completion of students with autism.

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First-Year Progession and Retention of Autistic Students in Higher Education

Progress and dropout of autistic students in the first year of higher education: a propensity-score-weighted population study. Published in ‘Autism in Adulthood’, June 2020.

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Background and enrollment characteristics of students with autism in higher education

Article on the background and enrolment characteristics of students with autism in higher education. Published in ‘Research in Autism Spectrum Disorders’, volume 67, November 2019.

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