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., Rienties, B., & Nguyen, Q. (2020). Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application. PLOS ONE, 15(6), [e0233977]. https://doi.org/10.1371/journal.pone.0233977
Tempelaar, D. (2020). Supporting the less-adaptive student: the role of learning analytics, formative assessment and blended learning. Assessment & Evaluation in Higher Education, 45(4), 579-593. . https://doi.org/10.1080/02602938.2019.1677855
Tempelaar, D., Rienties, B., & Nguyen, Q. (2020). Individual differences in the preference for worked examples: lessons from an application of dispositional learning analytics. Applied Cognitive Psychology. https://doi.org/10.1002/acp.3652
Tempelaar, D., Nguyen, Q., & Rienties, B. (2020). Learning Feedback Based on Dispositional Learning Analytics. In M. Virvou, E. Alepis, G. A. Tsihrintzis, & L. C. Jain (Eds.), Machine Learning Paradigms: Advances in Learning Analytics (Vol. 158, pp. 69-89). Springer. Intelligent Systems Reference Library book series, Vol.. 158
Tempelaar, D., Rienties, B., & Nguyen, Q. (2019). Learning engagement, learning outcomes and learning gains: lessons from LA. In D. G. Sampson, D. Ifenthaler, P. Isaías, & M. L. Mascia (Eds.), Proceedings of the 16th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2019) (pp. 257-264). IADIS Press.
Rienties, B., Tempelaar, D., Nguyen, Q., & Littlejohn, A. (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior, 100, 345-357. https://doi.org/10.1016/j.chb.2019.07.007
Tempelaar, D. (2019). Digitaal toetsen van wiskunde in het HO. Examens, 2019(3), 3-13. .
Tempelaar, D., Rienties, B., & Nguyen, Q. (2019). Analysing the Use of Worked Examples and Tutored and Untutored Problem-Solving in a Dispositional Learning Analytics Context. In H. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019) (Vol. 2, pp. 38-47). SCITEPRESS. Proceedings of the International Conference on Computer Supported Education, CSEDU
Tempelaar, D., Rienties, B., & Nguyen, Q. (2018). A multi-modal study into students’ timing and learning regulation: time is ticking. Interactive Technology and Smart Education, 15(4), 298-313. . https://doi.org/10.1108/ITSE-02-2018-0015
Mittelmeier, J., Rienties, B., Tempelaar, D., Hillaire, G., & Whitelock, D. (2018). The influence of internationalised versus local content on online intercultural collaboration in groups: A randomised control trial study in a statistics course. Computers & Education, 118(1), 82-95. https://doi.org/10.1016/j.compedu.2017.11.003