Educational data mining has become an effective tool for exploring the hidden relationships
in educational data and predicting students' academic achievements. This study proposes a …

The abundance of accessible educational data, supported by the technology-enhanced
learning platforms, provides opportunities to mine learning behavior of students, addressing …

Explainable Artificial Intelligence (XAI) has garnered significant attention in recent years to
increase the transparency of AI models and systems and aid in decision-making. This is …

Student success rate is a significant indicator of the quality of the educational services
offered at higher education institutions (HEIs). It allows students to make their plans to …

In our age of big data and growing computational power, versatility in data analysis is
important. This study presents a flexible way to combine statistics and machine learning for …

About one-third of college students drop out before finishing their degree. The majority of
those remaining will take longer than 4 years to complete their degree at “4-year” …

In institutional research, modern data mining approaches are seldom considered to address
predictive analytics problems. The goal of this paper is to highlight the advantages of tree …

As instruction shifts away from traditional approaches, online learning has grown in
popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics …

The present paper presents a relatively new non-linear method to predict academic
achievement of high school students, integrating the fields of psychometrics and machine …

The purpose of this study is to use machine learning and exploratory data analysis to
interrogate patterns of metrics from a national-level student survey. Analysis of over 1.8 …