Knowledge assessment: tapping human expertise by the QUERY routine

Kambouri, Maria; Koppen, Mathieu; Villano, Michael; Falmagne, Jean‐Claude

doi:10.1006/ijhc.1994.1006

Cited by 52 publications

(31 citation statements)

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“…In future research, we plan to implement other fit measures such as the di (discrepancy) index (Kambouri et al 1994) or the CA (correlational agreement) coefficient (van Leeuwe 1974). Functions for computing confidence intervals and for performing hypothesis tests for the diff and other fit measures will also be implemented.…”

Section: Resultsmentioning

confidence: 99%

“…Since |K| generally tends to be prohibitively large in practice, parameter estimation and model testing based on classical maximum likelihood methodology are not feasible in general (for details, seeÜnlü 2006, 2007). For instance, in an experiment by Kambouri (1991) (see also Kambouri et al 1994), reviewed in Doignon and Falmagne (1999), the number of knowledge states ranges from several hundreds to several thousands (for 50 items). In such cases, without any restrictions, it may be infeasible to obtain reliable estimates of the several hundreds to several thousands of model parameters, given a specific knowledge structure.…”

Section: Remarks Regarding the Basic Local Independence Modelmentioning

confidence: 99%

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DAKS: AnRPackage for Data Analysis Methods in Knowledge Space Theory

Ünlü¹,

Sargin²

2010

J. Stat. Soft.

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Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples.

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Section: Resultsmentioning

confidence: 99%

Section: Remarks Regarding the Basic Local Independence Modelmentioning

confidence: 99%

DAKS: AnRPackage for Data Analysis Methods in Knowledge Space Theory

Ünlü¹,

Sargin²

2010

J. Stat. Soft.

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“…For example, Kambouri et al (1994) introduced a semi-automated algorithm to construct knowledge structures. They developed an application that combines the use of empirical and an interactive question-answer process with domain experts to successfully construct knowledge structures.…”

Section: Partial Order Knowledge Structures and Bayesian Networkmentioning

confidence: 99%

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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“…In contrast to hierarchical structures, item to item structures build structures among observable knowledge item themselves (Falmagne, Koppen, Villano, Doignon, & Johannesen, 1990;Doignon & Falmagne, 1999), bypassing concept links. A number of researchers have worked on the problem of building student models within this framework (Dowling & Hockemeyer, 2001;Kambouri, Koppen, Villano, & Falmagne, 1994). Our own work on Partial Order Knowledge Structures (POKS) (Desmarais, Maluf, & Liu, 1996;Desmarais & Pu, 2005) falls under this line of research as well.…”

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

confidence: 99%

Bayesian Student Models Based on Item to Item Knowledge Structures

Desmarais

Gagnon

2006

Innovative Approaches for Learning and Knowledge Sharing

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Abstract. Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item relationships, in accordance with the theory of knowledge spaces. We compare two Bayesian frameworks for that purpose, a standard Bayesian network approach and a more constrained framework that relies on a local independence assumption. Their performance is compared over their respective ability to predict item outcome and through simulations over two data sets. The simulation results show that both approaches can effectively perform accurate predictions, but the constrained approach shows higher predictive power than a Bayesian Network. We discuss the applications of item to item structure for cognitive modeling within different contexts.

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Knowledge assessment: tapping human expertise by the QUERY routine

Cited by 52 publications

References 0 publications

DAKS: AnRPackage for Data Analysis Methods in Knowledge Space Theory

DAKS: AnRPackage for Data Analysis Methods in Knowledge Space Theory

Learned student models with item to item knowledge structures

Bayesian Student Models Based on Item to Item Knowledge Structures

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