자연주의적 탐구조사(Naturalistic Inquiry)

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Using Multivariate Statistics

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

Preface Acknowledgements 1. Models of working memory: an introduction Priti Shah and Akira Miyake 2. Working memory: the multiple-component model Alan D. Baddeley and Robert H. Logie 3. An embedded-processes model of working memory Nelson Cowan 4. Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence and functions of the prefrontal cortex Randall W. Engle, Michael J. Kane and Stephen W. Tuholski 5. Modelling working memory in a unified architecture: an ACT-R perspective Marsha C. Lovett, Lynne M. Reder and Christian Lebiere 6. Insights into working memory from the perspective of the EPIC architecture for modelling skilled perceptual-motor and cognitive human performance David E. Kieras, David E. Meyer, Shane Mueller and Travis Seymour 7. The soar cognitive architecture and human working memory Richard M. Young and Richard L. Lewis 8. Long-term working memory as an alternative to capacity models of working memory in everyday skilled performance K. Anders Ericsson and Peter F. Delaney 9. Interacting cognitive subsystems: modelling working memory phenomena within a multiprocessor architecture Philip J. Barnard 10. Working memory in a multilevel hybrid connectionist control architecture (CAP2) Walter Schneider 11. A biologically based computational model of working memory Randall C. O' Reilly, Todd S. Braver and Jonathan D. Cohen 12. Models of working memory: eight questions and some general issues Walter Kintsch, Alice F. Healy, Mary Hegarty, Bruce F. Pennington and Timothy A. Salthouse 13. Toward unified theories of working memory: emerging general consensus, unresolved theoretical issues and future research directions Akika Miyake and Priti Shah Indexes.

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Models of Working Memory: Mechanisms of Active Maintenance and Executive Control

Some usability and interpretability issues for single-strategy cognitive assessment models are considered. These models posit a stochastic conjunctive relationship between a set of cognitive attributes to be assessed and performance on particular items/tasks in the assessment. The models considered make few assumptions about the relationship between latent attributes and task performance beyond a simple conjunctive structure. An example shows that these models can be sensitive to cognitive attributes, even in data designed to well fit the Rasch model. Several stochastic ordering and monotonicity properties are considered that enhance the interpretability of the models. Simple data summaries are identified that inform about the presence or absence of cognitive attributes when the full computational power needed to estimate the models is not available.

/pdf/cognitive-assessment-models-with-few-assumptions-and-4yqxcn25li.pdf

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory:

A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models.

/pdf/learning-factors-analysis-a-general-method-for-cognitive-4cfh5kbsgd.pdf

Learning factors analysis – a general method for cognitive model evaluation and improvement

This paper demonstrates Markov chain Monte Carlo (MCMC) techniques that are particularly well-suited to complex models with item response theory (IRT) assumptions. MCMC may be thought of as a successor to the standard practice of first calibrating the items using E-M methods and then taking the item parameters to be known and fixed at their calibrated values when proceeding with inference regarding the latent trait. In contrast to this two-stage E-M approach, MCMC methods treat item and subject parameters at the same time; this allows us to incorporate standard errors of item estimates into trait inferences, and vice versa. We develop a MCMC methodology, based on Metropolis-Hastings sampling, that can be routinely implemented to fit novel IRT models, and we compare the algorithmic features of the MetropolisHastings approach to other approaches based on Gibbs sampling. For concreteness we illustrate the methodology using the familiar two-parameter logistic (2PL) IRT model; more complex models are treated in a subsequent paper (Patz & Junker, in press). Item response theory (IRT; Lord, 1980) models have greatly extended the data analytic reach of psychometricians, social scientists, and educational measurement specialists. The scaling and measurement applications of parametric IRT models in educational measurement are well known in areas ranging from basic questions about item quality and ability estimation (e.g., Linn, 1989) to equating (Holland & Rubin, 1982), differential item functioning (Holland & Wainer,

A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models

Patz and Junker (1999) describe a general Markov chain Monte Carlo (MCMC) strategy, based on Metropolis-Hastings sampling, for Bayesian inference in complex item response theory (IRT) settings. The...

Applications and Extensions of MCMC in IRT: Multiple Item Types, Missing Data, and Rated Responses.

A central assumption in the standard capture-recapture approach to the estimation of the size of a closed population is the homogeneity of the “capture” probabilities. In this article we develop an approach that allows for varying susceptibility to capture through individual parameters using a variant of the Rasch model from psychological measurement situations. Our approach requires an additional recapture. In the context of census undercount estimation, this requirement amounts to the use of a second independent sample or alternative data source to be matched with census and Post-Enumeration Survey (PES) data. The models we develop provide a mechanism for separating out the dependence between census and PES induced by individual heterogeneity. The resulting data take the form of an incomplete 23 contingency table, and we describe how to estimate the expected values of the observable cells of this table using log-linear quasi-symmetry models. The projection of these estimates onto the unobserved...

https://www.jstor.org/stable/pdfplus/2290811.pdf

Brian W. Junker

Papers

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory:

Learning factors analysis – a general method for cognitive model evaluation and improvement

A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models

Applications and Extensions of MCMC in IRT: Multiple Item Types, Missing Data, and Rated Responses.

A Three-Sample Multiple-Recapture Approach to Census Population Estimation with Heterogeneous Catchability