The Academic Anxiety Scale (AAS; Cassady, 2022; Cassady et al., 2019) is a measure of the construct academic anxiety, which is a generalized representation of anxieties experienced by learners in educational settings. Academic anxiety has been identified as a preclinical indicator of anxiety that provides important predictive utility to clinical…

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…

Double-scoring constructed-response items is a common but costly practice in mixed-format assessments. This study explored the impacts of Targeted Double-Scoring (TDS) and random double-scoring procedures on the quality of psychometric outcomes, including student achievement estimates, person fit, and student classifications under various…

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several…

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…

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping,…

The COVID-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students' learning behavior, affecting their overall performance. Thus, students' academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them…

School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common…

We report six experiments that examine how two essential components of a category-learning paradigm, training and feedback, can be manipulated to maximize learning and transfer of real-world, complex concepts. Some subjects learned through classification and were asked to classify hypothetical experiment scenarios as either true or non-true…

Student growth percentiles (SGPs) have become a common means to measure and report on student academic growth for state education accountability, and some states have adopted SGP cutscores as a means of classifying student growth into categories like "high/medium/low" growth. It has therefore become important to understand properties of…

Across the United States, suspension bans have become a popular policy response to address excessive and inequitable use of suspension in schools. However, there is little research that examines what strategies school staff employ when suspension is no longer permitted. I examine the effect of New York City's suspension ban on the use of a…

In this study, an early warning system predicting first-year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first-year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental…

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…

Why do inequalities in schooling persist, even in relatively egalitarian school systems? This article examines within school sorting as an explanation. We use classroom data on friendship networks in 480 European secondary schools and contrast comprehensive (England, Sweden) and tracked systems (Germany, Netherlands). Our question is to what…