Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies.…

The need for a knowledge-based society has perpetuated an increasing demand for higher education around the globe. Recently, there has been an increase in the demand for Computer Science professionals due to the rise in the use of ICT in the business, health and education sector. The enrollment numbers in Computer Science undergraduate programmes…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…

An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…

This article describes a model that can be used to analyze student enrollment data and can give insights for improving retention of part-time students and refining institutional budgeting and planning efforts. Adult higher-education programs are often challenged in that part-time students take courses less reliably than full-time students. For…

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…