Research has shown that the development of cognitive and social skills in preschool predicts school readiness in kindergarten. However, most longitudinal studies are short-term, tracking children's development only through the early elementary school years. This study aims to investigate the long-term impact of preschool predictors, intelligence,…

In order to make appropriate educational recommendations, psychologists must understand how cognitive test scores influence specific academic outcomes for students of different ability levels. We used data from the WISC-V and WIAT-III (N = 181) to examine which WISC-V Index scores predicted children's specific and broad academic skills and if…

The current research is the study of academic buoyancy in relation to emotional intelligence and parenting styles. Academic buoyancy is a strength in a student's life to deal with the routine problems in classroom study like low grades, negative feedback by teachers, and difficulties in understanding of concepts. For the studying the relationship…

Fluid intelligence and conscientiousness are important predictors of students' academic performance and competence gains. Although their individual contributions have been widely acknowledged, less is known about their potential interplay. Do students profit disproportionately from being both smart and conscientious? We addressed this question…

A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption…

This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of N = 6514 college students spanning 11 years (from 2009 to 2019) provided…

Numerous artificial intelligence methods have lately been applied in education. This study proposes an Adaptive Neural-network-based Fuzzy Logic (ANFIS) model combining fuzzy logic and artificial neural networks for predicting students' STEM attitudes. The inputs of the research were determined as grade levels and academic achievement scores, and…

Although numerous studies show that intelligence, measured by various tests, is a significant predictor of school achievement, this cognitive variable can only explain about 50% of the variance. It is also known that communicative language ability represents an important basis for learning subject content in the early period of formal education.…

The integration of educational data mining and deep neural networks, along with the adoption of the Apriori algorithm for generating association rules, focuses to resolve the problem of misdirection of students in the university, leading to their failure and dropout. This is reached through the development of an intelligent model that predicts the…

Emotional intelligence, self-concept, academic stress and personality have been associated with university students' academic performance. The aim of this paper was to study the relationship between self-concept and academic stress in Education students from different Universities in the region of Andalusia (Spain), analysing the mediational role…

The actiotope model of giftedness (AMG) highlights the interactions between the individual and the environment. Educational and learning capital (ELC) are essential resources that promote the development of excellence. The study objectives were to examine the contribution of educational capital (EC), learning capital (LC), and general intelligence…

While intelligence and motivational variables are well-established predictors of academic achievement, Need for Cognition (NFC), the stable intrinsic motivation to engage in and enjoy challenging intellectual activity, has not yet been considered comprehensively in this field, especially not longitudinally. By applying latent change score…

The performance history of 277 students in clothing and textile from two tertiary institutions in southern Nigeria was studied by artificial neural networks (ANN) and analysis of variance (ANOVA) in terms of institution, gender, ordinary level (O-level) qualification, marital status, and age. The study was guided by five research questions and…

In this study, associations among emotional intelligence, background variables, and academic performance in prelicensure nursing students enrolled in associate and baccalaureate degree nursing programs are explored. A gap in the literature exists regarding the specific background variables utilized in this study and the population. This study used…

Recently, artificial intelligence (AI) technologies have been widely used in the field of education, and artificial intelligence in education (AIEd) has gained increasing attention. However, no quantitative meta-analysis has been conducted on the overall effectiveness of AI on learning achievement and learning perception. To close this research…