On the spectra of soft-hard and pure-applied disciplines, geomatics engineering can be categorized as hard and applied, similarly to other engineering disciplines. One can expect that geomatics engineering would score lower in deep learning as such patterns have been observed for other engineering disciplines compared to soft and pure ones. Some students in upper level courses in geomatics engineering may still struggle with fundamental knowledge from lower level courses. This makes it hard for instructors to create an environment for deep learning. They may have to spend a significant amount of class time reviewing basic concepts, and not as much time is left for building up more complex concepts and problem solving. In order to be more successful in tackling higher level learning outcomes, it would be useful to identify areas of troublesome knowledge and specific threshold concepts in key geomatics engineering courses. By addressing these concepts, instructors can eliminate, or at least minimize, the bottlenecks in the learning process. This is the aim of the teaching and learning research study presented in this paper.The main method for collecting data for this study is classroom observations complemented by minute papers at the end of each course unit. Even though the study is in its early stage, some correlations between the type of lessons delivered and the student cognitive and behavioural engagement can be seen, and some concepts can already be identified as probable threshold concepts. As far as the authors are aware, this is the first study on threshold concepts in geomatics engineering
Insights into teaching a complex skill: Threshold concepts and troublesome knowledge in electroencephalography (EEG)