Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of educational datasets further catalyse this interestingness, especially in…

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…

This paper describes a neural network model that can be used to detect at- risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail…

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…

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student's likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those "at risk" of…

This paper reports on the findings of an ESRC funded project that contributes to the evidence base underpinning contextualised approaches to undergraduate admissions in England. We show that the bolder use of reduced entry requirements for disadvantaged learners is necessary if ambitious new widening access targets set by the Office for Students…

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal…

Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a…

Accountability seems forever engrained into the K-12 environment, as has been the expectation of delivering quality education to school aged children and adolescents. Yet, repeated failure of this expectation has focused the public's and policy maker's attention on the limitations of major accountability systems. This paper explores applications…

In this article, we propose the concept of "Autonomic Cycle Of Learning Analysis Tasks" (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic…

Objective: In an effort to break away from the stale classifications of community college students that stem from the hegemonic perspective of previous literature, this work utilizes the perceptions of community college practitioners to demonstrate new ways of understanding the identities of community college students. Method: By utilizing Gee's…

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that globally enrol millions of people. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms…

Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders…

The aim of this study was to examine the experiences of international undergraduate students who are identified with learning disabilities and enrolled in universities in the United States. There is a dearth of studies investigating the unique needs and challenges of this population. This is the first study to explore the phenomenon of…

School counselors are expected to develop programs that promote academic success for all students, including those at risk for dropping out of school. Knowledge of key indicators of potential dropouts and current trends in dropout prevention research may assist school counselors in better understanding this complex issue. Implementing recommended…