Model-based reasoning about learner behaviour

Koning, K. de; Bredeweg, Bert; Breuker, Joost; Wielinga, B.J.

doi:10.1016/s0004-3702(99)00106-x

Cited by 33 publications

(37 citation statements)

References 29 publications

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“…These dependencies are defined such that on the one hand they represent conceptual notions that closely match human reasoning (Bredeweg and Schut, 1991;de Koning et al, 2000), while on the other hand they are grounded in mathematical formalisms allowing automated computation. Two typical examples of such dependencies are direct influences (changes caused by processes) and proportionalities (causal propagation of changes) (Forbus, 1984).…”

Section: Background and Characteristicsmentioning

confidence: 99%

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Garp3 — Workbench for qualitative modelling and simulation

Bredeweg

Linnebank

Bouwer

et al. 2009

Ecological Informatics

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a b s t r a c t a r t i c l e i n f oEasy to use workbenches for Qualitative Reasoning and modelling have been virtually nonexistent. This has a limiting effect on the use of this Artificial Intelligence technology and its uptake by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models of system behaviour. The workbench employs diagrammatic representations for users to interact with model content and simulation results, and provides seamless interoperability between the different modes of use. Domain experts can use Garp3 to create conceptual models in situations where numerical information is sparse or unavailable, or when they want to formalise their conceptual understanding of how systems behave. Garp3 can be applied to stakeholder management or dissemination activities to illustrate and explain phenomena, and facilitate discussion among participants. The workbench can also be used in formal education to have learners express concepts, or interact with existing models, and support them in developing their understanding of 'how things work'. Garp3 incorporates a range of techniques from Artificial Intelligence known as knowledge-based techniques. The main goal of this paper is to present the representation and reasoning methods of these techniques as they have been developed and fine-tuned within the Garp3 workbench. The focus hereby is on the symbolic, non-numerical calculations that are required to generate the state-graph of a system's behaviour efficiently, while taking into account that users need to be able to track and understand this reasoning, both in terms of the end result and the intermediate results it delivers.

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Section: Background and Characteristicsmentioning

confidence: 99%

“…This particularly provides leverage for usability of the software in educational contexts (cf. Bredeweg and Schut, 1991;De Koning et al, 2000).…”

Section: Reasoning: State-graph Generationmentioning

confidence: 99%

Garp3 — Workbench for qualitative modelling and simulation

Bredeweg

Linnebank

Bouwer

et al. 2009

Ecological Informatics

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“…In some cases, in ensuring that the student understands the explanation, educational systems use a set of check questions (Rose et al, 2001). Rich domain models, knowledge-based reasoning, and qualitative reasoning capabilities have been used in some of the instructional systems that guide students according to the domain models (Bredeweg and Forbus, 2004;de Koning et al, 2000). Studies of human tutorial dialogue show that students themselves occasionally try to check the reasoning behind the answers provided (Fox, 1993).…”

Section: Learn Deep Modelsmentioning

confidence: 99%

“…The limited capacity of a human learner's memory affects the number of topics that can be discussed or kept track of in the learning dialogue (Lefrancois, 1991;Clark and Haviland, 1977;de Koning et al, 2000;Sweller, 1988).…”

Section: Limit the Nesting Of The Topicsmentioning

confidence: 99%

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Incorporating tutoring principles into interactive knowledge acquisition

Kim¹,

Gil²

2007

International Journal of Human-Computer Studies

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This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher-student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.

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Qualitative modeling

Forbus

2010

WIRES Cognitive Science

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Qualitative modeling concerns the representations and reasoning that people use to understand continuous aspects of the world. Qualitative models formalize everyday notions of causality and provide accounts of how to ground symbolic, relational representations in perceptual processes. This article surveys the basic ideas of qualitative modeling and their applications from a cognitive science perspective. It describes the basic principles of qualitative modeling, and a variety of qualitative representations that have been developed for quantities and for relationships between them, providing a kind of qualitative mathematics. Three ontological frameworks for organizing modeling knowledge (processes, components, and field) are summarized, along with research on automatically assembling models for particular tasks from such knowledge. Qualitative simulation and how it carves up time into meaningful units is discussed. We discuss several accounts of causal reasoning about dynamical systems, based on different choices of qualitative mathematics and ontology. Qualitative spatial reasoning is explored, both in terms of relational systems and visual reasoning. Applications of qualitative models of particular interest to cognitive scientists are described, including how they have been used to capture the expertise of scientists and engineers and how they have been used in education. Open questions and frontiers are also discussed, focusing on relationships between ideas developed in the qualitative modeling community and other areas of cognitive science. WIREs Cogni Sci 2011 2 374-391 DOI: 10.1002/wcs.115 For further resources related to this article, please visit the WIREs website.

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Model-based reasoning about learner behaviour

Cited by 33 publications

References 29 publications

Garp3 — Workbench for qualitative modelling and simulation

Garp3 — Workbench for qualitative modelling and simulation

Incorporating tutoring principles into interactive knowledge acquisition

Qualitative modeling

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