Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…

Guided by self-determination theory and the bioecological model, this latent class analysis explores the patterns and predictors of supportive relationships in a national sample of early adolescents (N = 6,469). A six-class solution emerged: youth with (1) emotional, informational, and academic support from various adults and peers across home,…

The use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling…

The goal of this study was to identify the onset and magnitude of prediction of early cognition to adult socioeconomic outcomes. Specifically, we were interested in examining which cognitive skills measured at 15, 24, 36, and 54 months predict educational attainment and salary at age 26. Data (N = 1364, 52% male) included a diverse sample (76%…

This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior. In order to flexibly perceive and intervene in the "burnout state" and improve online learning…

The integration of artificial intelligence (AI) into augmentative and alternative communication (AAC) systems has revolutionized the way non-verbal individuals interact with their environment. AI-powered symbolic text prediction offers innovative solutions to enhance expressive and receptive communication, promoting autonomy and social inclusion.…

With the development of mobile Internet and digital technologies, online education platforms transcend time and space constraints to provide ubiquitous learning environments. However, high dropout rates and low pass rates pose a great challenge. Predicting student performance enables early identification of academic failure tendencies,…

This paper aims to showcase the value of implementing a Bayesian framework to analyze and report results from international large-scale assessments and provide guidance to users who want to analyse ILSA data using this approach. The motivation for this paper stems from the recognition that Bayesian statistical inference is fast becoming a popular…

Recent decades have seen a global shift in educational policy and practice towards various forms of "joining-up," through partnerships and networks. These networks have differing aims but are broadly geared towards increasing quality and/or innovation in educational provision, although many prove messy and problematic. Policy makers in…

In the present study, we examined how the perceived attainability and relatability of moral exemplars predicted moral elevation and pleasantness among both adult and college student participants. Data collected from two experiments were analyzed with Bayesian multilevel modeling to explore which factors significantly predicted outcome variables at…

Metacognition is important for successful goal-directed behavior. It consists of two main elements: metacognitive knowledge and online awareness. Online awareness consists of monitoring and self-regulation. Metacognitive sensitivity is the extent to which someone can accurately distinguish their own correct from incorrect responses and is an…

The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial…

Algorithms may be better at prediction than humans in a variety of contexts, but they are not perfect. A deeper understanding of the ways in which educators use and question algorithmic advice within their professional domain is needed. Educators are a particularly unique professional group, in comparison with the other groups studied in the…

Maternal sensitivity during an observed mother-child clean-up task at 18 months and maternal sensitivity during an observed mother-child free-play task at 18 months were tested as independent predictors of child internalizing symptoms, externalizing symptoms, social competence, and language development at 24 months. Participants (n = 292 mothers)…

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst…