A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…

Virtual Reality (VR) has been found useful to improve engagement and retention level of students, for some topics, compared to traditional learning tools such as books, and videos. However, a student could still get distracted and disengaged due to a variety of factors including stress, mind-wandering, unwanted noise, external alerts, and internal…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

Technological advances have significantly affected education, leading to the creation of online learning platforms such as virtual learning environments and massive open online courses. While these platforms offer a variety of features, none of them incorporates a module that accurately predicts students' academic performance and commitment.…

Student attrition represents one of the greatest challenges facing U.S. postsecondary institutions. Approximately 40 percent of students seeking a bachelor's degree do not graduate within 6 years; among nontraditional students, who make up half of the undergraduate population, dropout rates are even higher. In this study, we developed a machine…

Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing…

The artificial intelligence in education (AIED) community has produced technologies that are widely used to support learning, teaching, assessment, and administration. This work has successfully enhanced test scores, course grades, skill acquisition, comprehension, engagement, and related outcomes. However, the prevailing approach to adaptive and…

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is…

Early dropout of students is one of the bigger problems that universities face currently. Several machine learning techniques have been used for detecting students at risk of dropout. By using sociodemographic data and qualifications of the previous level, the accuracy of these predictive models is good enough for implementing retention programs.…

Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students' academic performance early using…

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial,…

The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…

Students' physical and digital lives are increasingly entangled. It is difficult to separate students' "digital" well-being from their offline well-being given that artificial intelligence increasingly shapes both. Within the context of education's fiduciary and moral duty to ensure safe, appropriate and effective digital learning spaces…

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…