Dr D.T. Tempelaar
My research interests focus on modeling student learning, and students’ achievements in learning, from an individual difference perspective. This includes:
- Dispositional learning analytics: ‘big data’ approaches to learning processes, with the aim to find predictive models for generating learning feedback, based on computer generated trace data, and learning dispositions.
- Empirical research in social cognitive learning theories: achievement motivation, implicit theories, epistemic and achievement learning emotions, and self-regulated learning.
- Research into students’ self-regulated learning preferences in technology enhanced (blended) learning environments, such as revealed preferences for using worked examples, tutored or untutored problem solving.
- Cultural diversity in education; participating in the MUSBE multidisciplinary research theme "Culture, Ethics and Leadership", CEL.
- Role of formative assessment in learning processes.
Research in the role and effectiveness of developmental education.
Most recent publications:
Tempelaar, D. (2022). STUDENTS USE OF LEARNING AIDS: LESSONS FROM LEARNING ANALYTICS. In D. G. Sampson, D. Ifenthaler, & P. Isaías (Eds.), PROCEEDINGS OF THE 19th INTERNATIONAL CONFERENCE on COGNITION AND EXPLORATORY LEARNING IN THE DIGITAL AGE (CELDA 2022) (pp. 131-138).  IADIS Press.
Tempelaar, D., & Niculescu, A. C. (2022). Types of boredom and other learning activity emotions: A person-centred investigation of inter-individual data. Motivation and Emotion, 46(1), 84-99. . https://doi.org/10.1007/s11031-021-09909-y
Hillaire, G., Rienties, B., Fenton-O’Creevy, M., Zdrahal, Z., & Tempelaar, D. (2022). Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classifier. In Open World Learning: Research, Innovation and the Challenges of High-Quality Education (pp. 171-185). Routledge/Taylor & Francis Group. Routledge Research in Digital Education and Educational Technology https://doi.org/10.4324/9781003177098
Tempelaar, D., & Rienties, B. (2022). Learning Analytics en de noodzaak van rijke data. Tijdschrift voor Hoger Onderwijs, 40(1).
Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). Dispositional Learning Analytics for Supporting Individualized Learning Feedback. Frontiers in Education, 6, . https://doi.org/10.3389/feduc.2021.703773
Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). Enabling Precision Education by Learning Analytics Applying Trace, Survey and Assessment Data. In M. Chang, NS. Chen, DG. Sampson, & A. Tlili (Eds.), 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 355-359).  IEEE. https://doi.org/10.1109/ICALT52272.2021.00114
Coenen, J., Golsteyn, B. H. H., Stolp, T., & Tempelaar, D. (2021). Personality traits and academic performance: Correcting self-assessed traits with vignettes. PLOS ONE, 16(3), . https://doi.org/10.1371/journal.pone.0248629
Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). The Contribution of Dispositional Learning Analytics to Precision Education. Educational Technology & Society, 24(1), 109-122. . https://drive.google.com/file/d/1EwxQWeFWclFQZtiKGC3lsnP9oiQoJduy/view?usp=sharing
Tempelaar, D. (2021). LEARNING ANALYTICS AND ITS DATA SOURCES: WHY WE NEED TO FOSTER ALL OF THEM. In D. G. Sampson, D. Ifenthaler, & P. Isaías (Eds.), Proceedings of the IADIS International Conference Cognition Exploratory Learning in the Digital Age (Vol. 18, pp. 123-130). IADIS Press. CELDA Proceedings
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Feedback Preferences of Students Learning in a Blended Environment: Worked Examples, Tutored and Untutored Problem-Solving. In H. C. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), Computer Supported Education: 11th International Conference, CSEDU 2019, Heraklion, Crete, Greece, May 2-4, 2019, Revised Selected Papers (pp. 51-70).  Springer, Cham. Communications in Computer and Information Science No. 1220 https://doi.org/10.1007/978-3-030-58459-7_3