This systematic review sought to expand our knowledge of the increasing research studies focusing on the empirical link between school leadership and student achievement. Using five different data sources (Scopus, WoS, ERIC, Google Scholar, and Retrospective Reference Harvesting Procedure), we created a data set of 144 journal articles.…

Multi-wave-cross-lagged-panel models (CLPMs) of directional ordering are a focus of much controversy in educational psychology and more generally. Extending traditional analyses, methodologists have recently argued for including random intercepts and lag2 effects between non-adjacent waves and giving more attention to controlling covariates.…

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the…

This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…

Integration is a valuable yet underutilized process in scientific literatures, including the achievement motivation literature. In this piece, we advocate for and illustrate the benefits of giving integration a central place within the achievement motivation literature. We pay particular attention to the hierarchical model of achievement…

In recent years, researchers have shifted from pathologizing Black youths, when investigating the Black-White achievement gap, to focusing on factors contributing to their resilience and success. A critical review was conducted of three widely cited asset-based theoretical frameworks that are used to examine African American students' academic…

This study compares the estimated grammar school effect in different regression models, and explains why previous evidence of the effectiveness of grammar school is mixed. Like most studies of school effectiveness evaluation, previous research on grammar school effect usually applies regression to control for confounding between-school factors and…

Principal leadership has been widely regarded as a powerful catalyst for school improvement and student learning. This article presents a multivariate meta-analysis of 42 empirical studies, published between 2000 and 2020, that examined the effects of principal leadership on student achievement in the United States. The focus is on the conceptual…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

Academic stress among adolescents can undermine academic achievement and harm mental health. Levels of academic stress vary considerably across countries and education systems, but little is known regarding the causes of this variation. In this paper, I develop a theoretical framework positing that stress will be lower in education systems that…

This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with "an RNN model with a long-term time-series in which the features during the entire course…

Political discord, social tensions, and global conflicts compel teachers to guide students in exploring diverse perspectives and engaging in constructive, respectful dialogues. Furthermore, the rapid evolution of technology, particularly artificial intelligence (AI), necessitates teachers and school leaders to consider entirely new paradigms in…