J. Liem

Garp3 - Workbench for Qualitative Modelling and Simulation

Garp3 is a domain independent, multi-platform, qualitative modelling and simulation environment. It allows modellers to articulate and refine their conceptual domain knowledge and analyse this knowledge through simulation. Garp3 has been successfully applied in Ecology and Sustainable Development (SD) and is freely available via (http://www.garp3.org). Garp3 and the NaturNet-Redime Project Ecologists in the NaturNet-Redime project (http://www.naturnet.org) are building qualitative models about sustainable development issues through several case studies. For this purpose the Garp3 workbench for building, simulating, and inspecting qualitative models was developed (Bredeweg e al, 2006). The main goals of the development was making qualitative reasoning technology usable for non-computer scientists by creating a uniform user interface, a diagrammatic visual language for representing model content, and graphical buttons to communicate the available user options and manipulations. Garp3 is implemented in SWI-Prolog (http://www.swi-prolog.org) and seamlessly integrates three previously developed software components: Garp2 for simulating models, Homer for building models, and VisiGarp for inspecting simulation results. To further support the ecologists in their modelling efforts a structured approach to modelling was developed. This framework helps modellers refine their initial ideas, represented in concept maps, into detailed conceptualisations, such as structural models, causal models, and expected model behaviour. The diagrams in the final steps of the framework are close to the actual modelling primitives used to implement qualitative models. The Sketch environment in Garp3 supports modellers with tools to create the required diagrams. These Sketches not only help modellers in creating a model, but also serve as a more general description of the final model for other users of the model. One of the main goals of the NaturNet-Redime project is to create a sustainable development curriculum that allows students to learn about specific issues through qualitative modelling and simulation. To advance this goal the case study models developed in project are being integrated into a single library of sustainability concepts. Students can run and adapt different scenarios, let Garp3 automatically gather the correct knowledge relevant to the simulation and predict the possible outcomes, and analyse the results. To support the integration of the different case study models multiple model support and copy/paste functionality have been added to Garp3. The copying functionality assures that models remain syntactically correct, avoids adding redundant knowledge, preserves existing knowledge, and merges conceptual knowledge in a semantically correct manner as much as possible. The copy functionality also allows modellers to reuse parts of

Towards a structured approach to building qualitative reasoning models and simulations

Successful transfer and uptake of qualitative reasoning technology for modelling and simulation in a variety of domains has been hampered by the lack of a structured methodology to support formalisation of ideas. We present a framework that structures and supports the capture of conceptual knowledge about system behaviour using a qualitative reasoning approach. This framework defines a protocol for representing content that supports the development of a conceptual understanding of systems and how they behave. The framework supports modellers in two ways. First, it structures and explicates the work involved in building models. Second, it facilitates easier comparison and evaluation of intermediate and final results of modelling efforts. We show how this framework has been used in developing qualitative reasoning models about three case studies of sustainable development in different river systems.

Garp3: a new workbench for qualitative reasoning and modelling

Easy to use workbenches for Qualitative Reasoning (QR) and Modelling are virtually nonexistent. This has a limiting effect on the use and uptake of the technology by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models. Garp3 can be used to, discover, capture, and share conceptual knowledge on how systems behave.

Semantic techniques for enabling knowledge reuse in conceptual modelling

Conceptual modelling tools allow users to construct formal representations of their conceptualisations. These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the modelling process. We propose to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of qualitative reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. This paper describes how semantic grounding techniques can be used during the creation of qualitative reasoning models, to bridge the gap between the imprecise user terminology and a well defined external common vocabulary. We also explore the application of ontology matching techniques between models, which can provide valuable feedback during the model construction process.

Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour

Scaffolding is a well-known approach to bridge the gap between novice and expert capabilities in a discovery-oriented learning environment. This paper discusses a set of knowledge representations referred to as Learning Spaces (LSs) that can be used to support learners in acquiring conceptual knowledge of system behaviour. The LSs are logically self-contained, meaning that models created at a specific LS can be simulated. Working with the LSs provides scaffolding for learners in two ways. First, each LS provides a restricted set of representational primitives to express knowledge, which focus the learners knowledge construction process. Second, the logical consequences of an expression derived upon simulating, provide learners a reflective instrument for evaluating the status of their understanding, to which they can react accordingly. The work presented here is part of the DynaLearn project, which builds an Interactive Learning Environment to study a constructive approach to having learners develop a qualitative understanding of how systems behave. The work presented here thus focuses on tools to support educational research. Consequently, user-oriented evaluation of these tools is not a part of this paper.

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.

Semantic feedback for the enrichment of conceptual models

Conceptual modeling is a complex task that requires domain specific knowledge as well as a good command of modeling techniques. In this paper we propose an approach that aims to capture relevant knowledge from an online pool of conceptual models. This knowledge is brought to the user in order to assist the construction of new conceptual models. With our method, relevant feedback is generated based on knowledge extracted from the pool of models. Such feedback, tailored to the current modeling process of the user, allows the model to be improved based on shared knowledge.

Assessing Learner-Constructed Conceptual Models and Simulations of Dynamic Systems

Learning by conceptual modeling is seeing uptake in secondary and higher education. However, assessment of conceptual models is underdeveloped. This paper proposes an assessment method for conceptual models. The method is based on a metric that includes 36 types of issues that diminish model features. The approach was applied by educators and positively evaluated. It was considered useful and the derived grades corresponded with their intuitions about the models quality.

Towards Assessing and Grading Learner Created Conceptual Models

Learning by creating models is an active form of learning, which is well suited to induce deep understanding of phenomena. But how to evaluated such models, and apply feedback accordingly? What makes a learner created model a good model? We present two methods to assess and grade conceptual models and report on the application of these to model-data obtained from learners in a summer science class.

Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

This paper presents a domain independent question generation and interaction procedure that automatically generates multiple-choice questions for conceptual models created with Qualitative Reasoning vocabulary. A Bayesian Network is deployed that captures the learning progress based on the answers provided by the learner. The likelihood of concepts being known or unknown on behalf of the learner determines the focus, and the question generator adjusts the contents of its questions accordingly. As a use case, the Quiz mode is introduced.