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 …

Educational institutions need to formulate a well-established data-driven plan to get long-
term value from their learning analytics (LA) strategy. By tracking learners' digital traces and …

Student drop-out is one of the most burning issues in STEM higher education, which induces
considerable social and economic costs. Using machine learning tools for the early …

The last few years have witnessed an upsurge in the number of studies using Machine and
Deep learning models to predict vital academic outcomes based on different kinds and …

Using data to understand learning and improve education has great promise. However, the
promise will not be achieved simply by AI and Machine Learning researchers developing …

Many educational institutions are using predictive models to leverage actionable insights
using student data and drive student success. A common task has been predicting students …

Deep learning models for learning analytics have become increasingly popular over the last
few years; however, these approaches are still not widely adopted in real-world settings …

Higher education institutions often struggle with increased dropout rates, academic
underachievement, and delayed graduations. One way in which these challenges can …

Poor academic performance of students is a concern in the educational sector, especially if it
leads to students being unable to meet minimum course requirements. However, with timely …

The education domain is growing at an exponential rate, maturing with the introduction of
innovative and improved offerings to the learning community aided by various Education …