Supporting conceptual knowledge capture through automatic modelling

Abstract Building qualitative models is still a difficult and lengthy endeavour for domain experts. This paper discusses progress towards an automated modelling algorithm that learns Garp3 models based on a full qualitative description of the system's behaviour. In contrast with other approache

Semantic enrichment of models in DynaLearn learning enviroment

In this work we present our contribution to the DynaLearn learning environment. DynaLearn is an interactive modeling tool for education based on the "learning by modeling" approach. DynaLearn allows students to build Qualitative Reasoning (QR) models to formally represent a domain of their interest. This process helps the students to get a better understanding of the domain and to predict the behavior of the modeled system in view of the possible changes

Acquiring Conceptual Knowledge about How Systems Behave

There is a need for software that supports learners in actively dealing with theoretical concepts by having them create models and perform concept prediction and explanation (e.g. [3,4,5]). DynaLearn seeks to address this by developing a domain independent Interactive Learning Environment (ILE) based on Qualitative Reasoning (QR) [1]. The QR vocabulary fits the nature of conceptual knowledge, and the explicit representation of these notions in the software provides the handles to support an automated communicative interaction that actually discusses and provides feedback at the conceptual level

Knowledge-Level Reflection

This paper presents an overview of the REFLECT project. It defines the notion of knowledge level reflection that has been central to the project, it compares this notion with existing approaches to reflection in related fields, and investigates some of the consequences of the concept of knowledge level reflection: what is a general architecture for knowledge level reflection, how to model the object component in such an architecture, what is the nature of reflective theories, how can we design such architectures, and what are the results of our actual experiments with such systems

DynaLearn: Architecture and Approach for Investigating Conceptual System Knowledge Acquisition

DynaLearn is an Interactive Learning Environment that facilitates a constructive approach to developing a conceptual understanding of how systems work. The software can be put in different interactive modes facilitating alternative learning experiences, and as such provides a toolkit for educational research

Qualitative Futures

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Graphical means for inspecting qualitative models of system behaviour

This article presents the design and evaluation of a tool for inspecting conceptual models of system behaviour. The basis for this research is the Garp framework for qualitative simulation. This framework includes modelling primitives, such as entities, quantities and causal dependencies, which are combined into model fragments and scenarios. Given a library of model fragments and a scenario describing an initial situation, the qualitative simulation engine generates predictions in the form of a state-transition graph. This rich knowledge representation has potential for educational purposes. However, communicating the contents of simulation models effectively to learners is not trivial. The predicate logic format used by Garp is not easy for non-experts to understand, and a simulation often contains so much information that it is difficult to get an overview while still having access to detailed information. To address these problems, a tool has been developed that generates graphical representations of the information contained in a qualitative simulation. This tool, named VisiGarp, incorporates a vocabulary of graphical elements for model ingredients and relationships, and combines these into interactive diagrams. VisiGarp has been evaluated by thirty students, with promising results, using a setup which included simulation results and exercises about Brazilian Cerrado ecology

Kunstmatige Intelligentie in het onderwijs: leren met interactieve kennisrepresentaties

Leren is een actief proces waarin kennis wordt geconstrueerd. Dit proces is voor iedere leerling anders en vraagt individuele ondersteuning. Onderwijsmaterialen en werkvormen zouden zich gedurende het leerproces steeds moeten aanpassen aan de specifieke behoefte van elke leerling. Ontwikkelingen in het veld van de Kunstmatige Intelligentie & Onderwijs zijn ondertussen zodanig vergevorderd dat interactieve software deze kennisconstructie van leerlingen inderdaad individueel en adequaat kan ondersteunen. Lector Didactiek van de Bètavakken dr. Bert Bredeweg bespreekt in zijn rede de kracht van interactieve kennisrepresentaties als medium voor kennisconstructie. Als voorbeeld zal hij ingaan op conceptueel modelleren als didactische vorm voor het creëren van kennis over het gedrag van dynamische systemen. Hij zal ook illustreren hoe slimme softwarecomponenten individuele leerlingen naar behoefte kunnen ondersteunen bij dit leerproces