10.17608/k6.auckland.9848804.v1
Ellen-Wien Augustijn
Ellen-Wien
Augustijn
Ourania Kounadi
Ourania
Kounadi
Tatjana Kuznecova
Tatjana
Kuznecova
Raul Zurita-Milla
Raul
Zurita-Milla
Teaching Agent-Based Modelling and Machine Learning in an integrated way
The University of Auckland
2019
Integrated Modelling
Agent-Based model
Machine Learning
Education
Geospatial Information Systems
2019-09-16 12:03:30
Conference contribution
https://auckland.figshare.com/articles/conference_contribution/Teaching_Agent-Based_Modelling_and_Machine_Learning_in_an_integrated_way/9848804
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.