The afternoon opened with remarks from Mariëtte Huizinga, Vice Dean Education at the Faculty of Behavioural and Movement Sciences (FGB), who then introduced the guest speaker, Femke Kirschner, an educational advisor at the Centre for Academic Teaching and Learning at Utrecht University. Kirschner supports UU lecturers in enhancing and renewing their teaching practices by applying academic theory and insights to their daily educational practice.
To achieve this, Kirschner employs the Scholarship of Teaching and Learning (SoTL) approach, which offers lecturers an eight-step process to gain insight into the impact of their teaching methods. Using theory and research as the foundation, they can assess whether their teaching practices achieve the desired learning outcomes for students.
In her presentation, Kirschner highlighted the first two steps of this plan, using practical example questions to demonstrate how this method works in practice. More information on the SoTL process can be found on the Centre for Academic Teaching and Learning website.
Master’s Thesis Award
Following the lecture was the presentation of the FGB Master’s Thesis Award, whose winner will represent the faculty in the VU-wide Master’s Thesis Award. Each of the nine master’s programmes within the faculty submitted a thesis, with titles listed below:
- RM Cognitive Neuropsychology: Justin Abu Hoof – Individuals with autism have difficulties learning implicit sequences of social interactions that require mentalising
- Ma Psychology: Kerem Özel – The age of remote work: too old to be competent?
- Ma Pedagogische Wetenschappen: Saskia Stam – Gezinsgerichte narratieve traumabehandeling, een verkenning
- RM Human Movement Sciences: Judith Nijensteen – Short, powerful and intensive? An evaluation of a new training program of firearms training at Dutch police academies
- RM Social Psychology: Isabel Franke – Seeds of tomorrow: how virtual trees grow better behavior
- Ma Muskuloskeletal Physiotherapy Sciences: Dávid Fábián – Identifying Post-COVID patients based on cardiopulmonary exercise test results with machine learning
- RM Genes in Brain and Health: Britt Min – Like Parent, Like Child: A Meta-Analysis on Parent-Offspring Resemblance in Reading Ability
- RM Clinical Developmental Psychology: Karl Weinreich – How Does Problem Management Plus Change Symptom Networks in Syrian Refugees - A Network Analysis
- Ma Human Movement Sciences: Niels Fontijne – Thermophysiology during Cycling: the first steps to a mathematical model
The high quality of all the theses made it difficult to compare across fields, so the final nominee was chosen by lottery. The winner is Dávid Fábián from the Master’s in Musculoskeletal Physiotherapy Sciences, with his thesis titled Identifying Post-COVID patients based on cardiopulmonary exercise test results with machine learning.
Fábián’s thesis explores how machine learning can aid in better understanding the physical limitations of post-COVID patients through the Cardiopulmonary Exercise Test (CPET). He utilised self-organising maps (SOM) to analyse the test results of post-COVID patients compared to healthy individuals, revealing patterns that are often missed using traditional methods.