Learning Analytics provide better means of interaction and guidance between educators and students. Through data, we can elaborate on our understanding of the way a student learns and progresses in the education environment or learning management system. Adaptive and blended learning as modern education models can further emphasize the role of learning analytics: since the teacher is no longer present physically in all (or any) learning scenarios or is partially available, the significance of data collection, analysis and reaction models have become crucial. Moreover, we can also administer pre-emptive measures to ensure continued progression by using the data in prediction models. In this presentation, we describe a learning analytics project between several Finnish universities of applied sciences. The focus is on several empirical experiments conducted in one of the participating universities. We discuss the design and the setup of the cases along with our findings on the effectiveness to student performance and motivation, and teachers and students’ perceptions of the experiments. Moreover, the ethical aspect of the experiment is observed along with limitations of the cases. We conclude by providing our lessons learned and by offering some hints and tips for other researchers, who might be conducting similar experiments later.
Matias Nevaranta, Satakunta University of Applied Sciences, Finland
Katja Lempinen, Satakunta University of Applied Sciences, Finland
Erkki Kaila, University of Turku, Finland
Stream: Teaching Experiences
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