Knowledge Spaces

Doignon, Jean-Paul; Falmagne, Jean‐Claude

doi:10.1007/978-3-642-58625-5

Cited by 363 publications

(346 citation statements)

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“…Doignon and Falmagne [3,4] developed the theory of knowledge spaces originally as a behaviouristic approach to adaptive assessment of knowledge. The core idea is to describe a domain of knowledge by a set of test items, and to structure this set of test items by prerequisite relationships.…”

Section: Competence Based Knowledge Space Theorymentioning

confidence: 99%

Applying a Web and Simulation-Based System for Adaptive Competence Assessment of Spinal Anaesthesia

Hockemeyer

Nussbaumer

Lövquist

et al. 2009

Advances in Web Based Learning – ICWL 2009

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Abstract. The authors present an approach for implementing a system for the assessment of medical competences using a haptic simulation device. Based on Competence based Knowledge Space Theory (CbKST), information on the learners' competences is gathered from different sources (test questions, data from the simulator, and supervising experts' assessments).The envisaged architecture consists of three core modules, an LMS (Moodle) containing user model and content objects and realising the interface between system and user, a simulator interface as an own service connecting the LMS to the (external) simulator system, and a CbKST service offering the assessment logic and visualisations of the assessment result for learner and teacher.

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Section: Competence Based Knowledge Space Theorymentioning

confidence: 99%

Applying a Web and Simulation-Based System for Adaptive Competence Assessment of Spinal Anaesthesia

Hockemeyer

Nussbaumer

Lövquist

et al. 2009

Advances in Web Based Learning – ICWL 2009

View full text Add to dashboard Cite

show abstract

“…For example, a sound theoretical foundation in psychometric measurement has been developed behind IRT. Similarly, the theory of knowledge spaces (Doignon and Falmagne, 1999) also offers a strong formal basis for student model upon which a few models are based, including the POKS framework we will discuss later (section 3).…”

Section: Tradeoffs Between Student Modelsmentioning

confidence: 99%

“…They emerge from the work of Falmagne et al (1990) and Doignon and Falmagne (1999) on the theory of knowledge spaces. Our own work on Partial Order Knowledge Structures (POKS) (Desmarais et al, 1996;Desmarais and Pu, 2005) falls under this line of research as well.…”

Section: Item To Item Node Structures and The Theory Of Knowledge Spacesmentioning

confidence: 99%

“…Item to item structures are based on a cognitive modeling theory named knowledge spaces (Doignon and Falmagne, 1999). The theory of knowledge spaces asserts that knowledge items, i.e.…”

Section: Knowledge Spacesmentioning

confidence: 99%

“…Formally, it can be shown that if the space of individual knowledge states is closed under union, then that knowledge space-the set of all possible knowledge states-can be represented by an AND/OR graph (Doignon and Falmagne, 1999). In other words, if we combine two individuals' knowledge states, then that combined knowledge state is also plausible (i.e.…”

Section: Knowledge Spacesmentioning

confidence: 99%

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Learned student models with item to item knowledge structures

Desmarais

Meshkinfam

Gagnon

2006

User Model User-Adap Inter

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Abstract. Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, POKS, relies on a naive Bayes framework whereas the other is based on the Bayesian network learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in different experimental simulations. The results from simulations over three data sets show that they both can effectively perform accurate predictions, but POKS generally displays higher predictive power than the Bayesian network. Moreover, the simplicity of POKS translates to a time efficiency of one to three orders of magnitude greater than the Bayesian network runs. We furhter explore the use of the item to item approach for handling concepts mastery assessment. The approach investigated consist in augmenting an initial set of observations, based on inferences with the item to item structure, and feed the augmented set to a Bayesian network containing a number of concepts. The results show that augmented set can effectively improve predictive power of a Bayesian network for item outcome, but that improvement does not transfer to the concept assessment in this particular experiment. We discuss different explanations for the results and outline future research avenues.

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Representational Measurement Theory

Luce

Suppes

2002

Stevens' Handbook of Experimental Psychology

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This chapter focuses on empirical structures, with an ordering attribute, leading to numerical or geometric representations. A historical sketch of physical, behavioral, and social science measurement until 1950 is followed by modern approaches to representational measurement in the behavioral sciences. Among these structures are: difference, additive conjoint, averaging, and non‐additive concatenation and conjoint structures. Derived measurement is described by distributive linkages between two structures with a common attribute and is exemplified by physical, utility, and psychophysical examples. Two mathematical results are needed in each case: the existence of structure‐preserving numerical representations and their uniqueness . The latter has been analyzed generically. Using these ideas, models of magnitude estimation and production are summarized. The nature of axiomatization is discussed in terms of types of logical languages, the role of certain second‐order axioms, and issues of consistency and independence of axioms. Important results about the impossibility of simple axiomatizations of classes of finite structures are described along with mention of some problems.

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Knowledge Spaces

Abstract: The use of general descriptive names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 363 publications

References 0 publications

Applying a Web and Simulation-Based System for Adaptive Competence Assessment of Spinal Anaesthesia

Applying a Web and Simulation-Based System for Adaptive Competence Assessment of Spinal Anaesthesia

Learned student models with item to item knowledge structures

Representational Measurement Theory

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