Predictors of academic success at university are of great interest to educators, researchers and policymakers. With more students studying online, it is important to understand whether traditional predictors of academic outcomes in face-to-face settings are relevant to online learning. This study modelled self-regulatory and demographic predictors…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

The present study examined demographic and academic predictors of astronomy performance among a cohort of N=1909 community college students enrolled in astronomy courses in a large suburban community college during a four-year time frame, 2015-2019. The theoretical framework was based upon a deconstructive approach for predicting community college…

Given the rapid growth of K-12 online learning, research is needed in the effective identification of at-risk students so that administrators and teachers can develop appropriate supports and interventions. The purpose of this research was to determine if student success in an online course could be predicted for English Learners (EL) using…

Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…

School shootings are at an all-time high. That's according to the National Center for Education Statistics, which has been keeping track of the numbers for about 20 years. What are schools to do? Is there a "profile" of the typical school shooter that could help us identify those who might commit a shooting in the future? Is there some…

Despite the importance of obtaining a university degree, retention rates remain a concern for many universities. This longitudinal study provides a multi-domain examination of first-year student characteristics and behaviors that best predict which students graduate. Graduation status was assessed seven years after students entered university.…

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to…

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…

Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student…

This study aims to determine the levels of teacher and trainer candidates in the university education process, in line with the findings obtained in the dimensions of professional anxiety and self-efficacy and their sub-dimensions as a result of examining the professional anxiety and self-efficacy perceptions of teacher and trainer candidates. In…

Although studies have investigated educational attainment of groups of students professing low and high educational self-expectations, groups of noncommittal students, rather than being studied as a discrete group, have been treated as missing and ignored. This study investigated the differences between students of noncommittal, low, and high…

Large-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized…

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…