Abstract: Salles and Bredeweg, 2001
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Publication details
Salles and Bredeweg, 2001.
Constructing Progressive Learning Routes through Qualitative
Simulation Models in Ecology,
Proceedings of the International workshop on
Qualitative Reasoning, QR'01, pages 82-89.
(PDF)
A smaller version is published in: Artificial Intelligence in Education:
AI-ED in the Wired and Wireless Future. (eds) J.D. Moore,
G. Luckhardt Redfield, and J.L. Johnson,
pages 595-597, 2001, IOS-Press/Ohmsha, Japan, Osaka.
Abstract
Qualitative models support interactive simulations that are well
suited to help learners in acquiring causal interpretations of
physical systems and their behavior. Such simulation models can be
large, particularly if they include many subsystems. When simulations
are too big they hardly can be used effectively for teaching
purposes. They have to be reorganized into smaller sets of simulation
models and ordered in a sequence for the learner to progress
through. Model-dimensions and techniques such as Causal Model
Progression have been presented as means to address this problem. In
this paper we investigate how to decompose a large qualitative
simulation into a progressive sequence of smaller simulations, useful
for teaching purposes, in the domain of ecology. Based on notions
introduced by Causal Model Progression, the Genetic Graph, and the
Didactic Goal Generator, we have constructed a set of dimensions that
can be used in this respect. Following these dimensions we show how a
large qualitative simulation model of the Brazilian Cerrado vegetation
dynamics can be rearranged into a sequence of clusters, each
representing and simulating distinct features of such ecological
systems. These clusters are ordered in evolutionary model progression
lines according to movements from static to dynamic models and, by
incorporating structural changes, from less complex to more complex
models. The approach presented in this paper thus provides means, in
terms of knowledge characteristics, to effectively reorganize
qualitative simulation models for teaching purposes. In the discussion
we briefly argue that this approach may also be applicable to
qualitative simulation in other domains.
Last modified on June 6th, 2001
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