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Teaching Agent-Based Modelling and Machine Learning in an integrated way. GeoComputation 2019

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Version 2 2019-12-01, 23:11
Version 1 2019-09-16, 12:03
conference contribution
posted on 2019-12-01, 23:11 authored by Ellen-Wien Augustijn, Ourania Kounadi, Tatjana Kuznecova, Raul Zurita-Milla
The integration of Agent-Based Modelling (ABM) and Machine Learning (ML) provides many promising opportunities, yet this research field is underdeveloped. Different reasons are given for this lack of integration, including a shortage of behavioural data and technical implementation difficulties. However, we think that one crucial problem is being overlooked. In our educational system, we teach topics one by one and do not explicitly focus on the integration of various modelling paradigms. This is a missed opportunity that should be addressed, to prepare our students for a world where models are increasingly complex and where data and model integration becomes inevitable. In this paper, we share our experiences in a course in Geoinformatics, where integrated ABM and ML modelling is central. In our class, we use the Living Textbook to work on interlinked concept maps, and we have an overarching case study assignment. Preliminary outcomes show that students’ learning and project work could benefit from simplifying the case study assignment and introducing the parallel teaching of ABM and ML. In general, different teaching methods and setups still need to be explored, to ensure that our future model designers are well equipped for their task.



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