The nonparametric classification (NPC) method has been proven to be a suitable procedure for cognitive diagnostic assessments at a classroom level. However, its nonparametric nature impedes the obtention of a model likelihood, hindering the exploration of crucial psychometric aspects, such as model fit or reliability. Reporting the reliability and…

Although the generalized deterministic inputs, noisy "and" gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that…

Cognitive diagnostic models (CDMs) provide pedagogically relevant information in the form of a student profile of multiple binary categorizations of students into mastery or nonmastery statuses on latent traits called attributes. Federal educational accountability requires accountability measures to designate students into one of at least three…

To expand the use of cognitive diagnosis models (CDMs) to longitudinal assessments, this study proposes a bias-corrected three-step estimation approach for latent transition CDMs with covariates by integrating a general CDM and a latent transition model. The proposed method can be used to assess changes in attribute mastery status and attribute…

Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

Without the ability to analyze their own learning (metacognition), students can find it difficult to manage their educational journey. The good news is, future learning environments on online platforms offer innovative ways to develop these skills, even for those who are digital natives. This study aims to ascertain the effectiveness of online…

The present article provides a descriptive review of the studies conducted by eight memory researchers who empirically investigated their own autobiographical memory. They are Francis Galton, Madorah Smith, Marigold Linton, Willem Wagenaar, Steen Larsen, Dorthe Berntsen, Alan Baddeley and Richard White. These authors assessed their ability to…

Purpose: The contributions from the central auditory and cognitive systems play a major role in communication. Understanding the relationship between auditory and cognitive abilities has implications for auditory rehabilitation for clinical patients. The purpose of this systematic review is to address the question, "In adults, what is the…

Recent developments have enabled the modeling of longitudinal assessment data in a diagnostic classification model (DCM) framework. These longitudinal DCMs were developed to provide measures of student growth on a discrete scale in the form of attribute mastery transitions, thereby supporting categorical and criterion-referenced interpretations of…

Free-listing is a quick, semi-quantitative methodology commonly used by anthropologists to uncover information within a cultural domain. In this method note, we review how anthropologists have used free-listing in a variety of research settings. We then apply the social-ecological framework to describe how free-listing can be used for formative,…

Background: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. Aims:…

Background: New methods are constantly being developed to adapt cognitive load measurement to different contexts. However, research on middle childhood students' cognitive load measurement is rare. Research indicates that the three cognitive load dimensions (intrinsic, extraneous, and germane) can be measured well in adults and teenagers using…

As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner's cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages…

Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…