In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively…

We investigated the effect of careless or insufficient effort (C/IE) responses in a study using Amazon's Mechanical Turk. A factor mixture model was used to identify latent classes based on the pattern of responses with biases and examine the effect of C/IE responses on the fit of the theoretical model.

This study provides empirical evidences on the differentiation of the academic community amid the latest classified reform of faculty evaluation, highlighted by up-or-out policy in the non-research university context in China. The systematic data analysis sketches out faculty's segmentation and four characterizations including academic…

This paper presents a cartography of the digital literacy academic field. Such cartography is comprised of two sections: a categorization of the field through literature review and analysis, and an exploration of its main issues through thematic and network analysis. On the one hand, five conceptual categories of digital literacies are found:…

In this ethnographic study, I present a single school's practice of registering and analysing absence from school. I show that teachers use various "dirty," interpretational contexts for understanding absence and make it classifiable in "clean" attendance categories -- a move that decontextualises the meaning of absence. When…

Many innovations that are implemented in schools are initially successful, but fail to become part of the schools' habits and routines. Relatively little research has followed innovations in schools for a long(er) time. In addition, few reforms last long enough to be studied longitudinally. In this exploratory study, the authors aim to find a way…

During the summer of 2022, Ashley Greene, a professor at Lamar University in Beaumont, Texas, and a co-author of this article, began a discussion on American Sign Language (ASL) literature with her doctoral students. The students, most of whom had backgrounds in K-12 deaf education or ASL education, explored what ASL literature means, how such…

This study was conducted to further research into the impact of student-group item parameter drift (SIPD) --referred to as subpopulation item parameter drift in previous research-- on ability estimates and proficiency classification accuracy when occurring in the discrimination parameter of a 2-PL item response theory (IRT) model. Using Monte…

This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of…

In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…

Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…

Autism spectrum disorder (ASD) and Developmental Coordination Disorder (DCD) are distinct clinical groups with overlapping motor features. We attempted to (1) differentiate children with ASD from those with DCD, and from those typically developing (TD) (ages 8-17; 18 ASD, 16 DCD, 20 TD) using a 5-min coloring game on a smart tablet and (2)…

Purpose: A considerable number of screening and diagnostic tools for autism exist, but variability in these measures presents challenges to data harmonization and the comparability and generalizability of findings. At the same time, there is a movement away from autism symptomatology to stances that capture heterogeneity and appreciate diversity.…

This study proposes a Bayesian method for diagnostic classification models (DCMs) for a partially known Q-matrix setting between exploratory and confirmatory DCMs. This Q-matrix setting is practical and useful because test experts have pre-knowledge of the Q-matrix but cannot readily specify it completely. The proposed method employs priors for…

This study aims to determine the changes in the career barrier (CB) trajectory of South Korean school dropouts over time and to identify the number of groups that can be categorized according to CB trajectory. The study analyzed three years of panel data on school dropouts from the Korean National Youth Policy Institute, which comprises…