The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…

The article shows the role of data mining methods at the stages of the e-learning risk management for the various participants. The article proves the e-learning system fundamentally contains heterogeneous information, for its processing it is not enough to use the methods of mathematical analysis but it is necessary to apply the new educational…

Artificial Neural Networks (ANNs) have been proposed as a promising approach for the classification of students into different levels of a psychological attribute hierarchy. Unfortunately, because such classifications typically rely upon internally produced item response patterns that have not been externally validated, the instability of ANN…

Large-scale social surveys of visually impaired people often explore participants' mobility and travel behaviour. What is methodologically more challenging is gathering participant-centred data in relation to their own interpretation of the barriers they face. Findings from a national survey of visually impaired people are presented in this…

A six-year-old child was independently assessed by three licensed educational (school) psychologists and one interdisciplinary team in the UK. All but one of these practitioners believed their assessment to be the first. The aim was to compare the practice of assessors and their conclusions especially in diagnostic categorisation. The methods of…

Research has documented two effects of interfeature causal knowledge on classification. A "causal status effect" occurs when features that are causes are more important to category membership than their effects. A "coherence effect" occurs when combinations of features that are consistent with causal laws provide additional…

Classification testing has been widely used to make categorical decisions by determining whether an examinee has a certain degree of ability required by established standards. As computer technologies have developed, classification testing has become more computerized. Several approaches have been proposed and investigated in the context of…

Perhaps the greatest value of response to intervention (RTI) as a decision framework is that it brings attention to variables (e.g., mastery of prerequisite skills, frequency of instructional corrective feedback, reinforcement schedules for correct responding) that if changed might make a meaningful difference for students (e.g., child rate of…

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

In this commentary, the author points out few issues, one being that there are models mislabeled as diagnostic, which deal with linear decompositions of item difficulties rather than estimating multidimensional skill variables. The author discusses the issue that there are many new names for essentially well-known models for multiple simultaneous…

Diagnostic assessment is currently an active research area in educational measurement. Literature related to diagnostic modeling has been in existence for several decades, but a great deal of research has been conducted within the last decade or so, especially within the last five years. The author summarizes the key components in the application…

One promising application of diagnostic classification models (DCM) is in the area of cognitive diagnostic assessment in education. However, the successful application of DCM in educational testing will likely come with a price--and this price may be in the form of new test development procedures and practices required to yield data that satisfy…

Cognitive diagnostic models (CDMs) model the probability of correctly answering an item as a function of an examinee's attribute mastery pattern. Because estimation of the mastery pattern involves more than a continuous measure of ability, reliability concepts introduced by classical test theory and item response theory do not apply. The cognitive…

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only…

"Differentiating Instruction With Menus Grades 3-5" offers teachers everything they need to create a student-centered learning environment based on choice. Addressing the four main subject areas (language arts, math, science, and social studies) and the major concepts taught within these areas, these books provide a number of different types of…