The current article reassessed the views expressed in two articles written 40 years ago about the core deficit, variability, relapse and recovery, and the role of attention in stuttering. Motor factors are now generally agreed to be the core deficit underlying stuttering, but they interact dynamically over time with other factors (Smith &…

Futures of education are increasingly defined through predictive technologies and methods. We conceptualize 'algorithmic futuring' as the use of data-driven digital methods and predictive infrastructures to anticipate educational futures and animate actions in the present towards their materialization. Specifically, we focus on algorithmic…

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying…

In this conceptual paper, we aim to revisit key research themes in contemporary organizational institutionalism and by doing this, redirect attention of scholars in public administration towards the most promising domains of application of institutional theory. We propose to shift attention from enabling and power-induced framing of institutional…

Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of…

Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of…

Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…

The artificial intelligence methods might be applied to see through the education problems, and make effective prediction and decision. The transformation from data to decision are inseparable from the learning analytics. In order to solve the dynamic multi-objective decision problems, a decision learning algorithm is designed to analyze the…

Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…

Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed…

With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…

The present work focuses on the performance of two types of shrinkage priors--the horseshoe prior and the recently developed regularized horseshoe prior--in the context of inducing sparsity in path analysis and growth curve models. Prior research has shown that these horseshoe priors induce sparsity by at least as much as the "gold…

The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality.…

Today, the main aim of educational institutes is to provide a high level of education to students, as career selection is one of the most important and quite difficult decisions for learners, so it is essential to examine students' capabilities and interests. Higher education institutions frequently face higher dropout rates, low academic…

In this essay, the authors examine a congressional proposal -- the Educational Choice for Children Act (ECCA) -- to allow taxpayers to redirect their federal tax dollars to organizations that would provide funding for students to enroll in private school or to help pay for outside educational supports. Based on the implementation of similar…