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Development and assessment of ecological models in the context of the European Water Framework Directive: key issues for trainers in data-driven modeling approaches
Everaert, G.; Pauwels, I.S.; Boets, P.; Buysschaert, F.; Goethals, P.L.M. (2013). Development and assessment of ecological models in the context of the European Water Framework Directive: key issues for trainers in data-driven modeling approaches. Ecological Informatics 17: 111-116. https://hdl.handle.net/10.1016/j.ecoinf.2012.10.007
In: Ecological Informatics. Elsevier: Amsterdam. ISSN 1574-9541; e-ISSN 1878-0512, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Author keywords
    Classification and regression trees; Course efficiency; Data mining; Education

Auteurs  Top 
  • Buysschaert, F.
  • Goethals, P.L.M., meer

Abstract
    Teaching students to develop data-driven models is a challenging task as a good balance has to be found between the theoretical background of the models, the ecological relevance of the knowledge rules inferred and their socio-economic applicability. In this context it is unclear which aspects of the modeling process are easily understood by students, and in particular, how theoretical issues interfere with practical boundary conditions and socio-economic relevance (ecosystem protection, water management, policy development, ecological engineering). In order to fill this knowledge gap, students developed static data-driven models and tutors assessed students' performances. Criteria such as the theoretical, ecological and socio-economic relevance of the derived knowledge rules were used to select the most optimal models.We noticed an inverse relationship between the complexity of the subtasks and the number of students that succeeded. Students evaluated their models with respect to the theoretical reliability, but were not likely to consider the other two criteria. Half of the students succeeded in assessing the models based on their ecological relevance and only 17% of the students checked the socio-economic relevance of the knowledge rules. Four groups out of seven assessed their models merely based on the predictive power of the models. Only one group integrated the theoretical, ecological and socio-economic relevance to assess the models.The key findings of our research can be used to optimize the efficiency of data mining courses. We reveal which aspects of the modeling process students seem to overemphasize and give recommendations about the topics trainers should emphasize in the future to ensure that students develop advanced skills. Based on our results the theory–practice dichotomy in higher education can be further reduced. Our learning-by-doing approach showed students how to solve common problems in ecological data sets (e.g. missing data, outliers, collinearity, non-normal distribution, parameterization, uncertainty, etc.), which are often only briefly discussed in basic statistical courses.

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