A still further note on freeman's measure of association

This article demonstrates that Freeman's theta, a measure of association between a nominal variable and an ordinal variable, has a sampling distribution identical with the sampling distribution of Mann-Whitney'sU.     

Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.     

Competition in lexical processing of Italian noun/verb homographs

The study investigates the lexical representation of Italian noun/verb homographs, introducing a factor which has been analysed in production studies: the specific-word frequency (SWF). The SWF parameter allows to understand whether the grammatical class information is specified within the lexical knowledge or it is selected during post-lexical stages. We ran six lexical decision tasks employing the priming paradigm and a grammatical decision task to investigate the lexical representation of noun/verb homographs. In addition, we sought to determine the role played by the frequency of representations and the temporal activation of the grammatical class information. Our findings support the claim that these forms have independent representations for each syntactic role they can play. We interpreted these findings in the light of what we call the “Nominal Dominance” effect, which affects the lexical access of noun/verb homographs. We also discuss the relation between nominal dominance and lexical frequency.     

Produire et réviser la morphologie flexionnelle du nombre : de l'accès à une expertise

To understand how children and adults produce written text, you must first be able to describe how they acquire some basic mechanisms. This is the reason to study the acquisition and use of simple orthographic principles. Following this idea, the research of Largy, Fayol et al. concerns nominal and verbal agreement in French. This article proposes a synthesis of the recent development in this domain. Through the study of two competences to be acquired by the writer — (1) to produce the flexional morphology of number in written text and (2) to revise and judge the correctness of this production — this article presents and discusses in which way the learner access to a writing expertise, always relative.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

An Algorithm to Improve Test Answer Copying Detection Using the Omega Statistic

The omega (ω) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected copied responses through probability sampling and bootstrapping. In doing so, the bias in copier ability estimation will be determined and used to update the ability estimate for calculating the modified omega (ω^m), a new statistic based on the ω. The performance of ω^m and ω were compared in a Monte Carlo simulation study under 40 typical testing conditions (2 test lengths x 4 sample sizes x 5 levels of copying). In almost all conditions, the ω^m had the same or better controlled Type I error and higher power than ω. The increase in power was particularly eminent when the source's estimated ability was higher than the copier and when 20% or 30% of items were copied. These findings support the use of the ω^m as a replacement of ω to detect answer copying in multiple choice exams.