An Analysis of Learning Analytics in Smart Education Systems and Industry 5.0 Curriculum Alignment

Abstract

The growing adoption of smart education systems has resulted in extensive digital records of student learning activities, creating new possibilities for analysing academic progress through learning analytics. These data-driven approaches allow educational institutions to move beyond traditional evaluation methods and gain deeper insights into student performance patterns. This paper examines how learning analytics can be applied to support both the prediction of academic outcomes and the improvement of student performance in digitally enabled learning environments. The analysis is based on educational datasets containing information related to learner demographics, prior academic results, attendance behaviour, and engagement with online learning platforms. Statistical methods are used to explore relationships among learning variables, while predictive techniques based on regression and classification models are employed to estimate academic performance and detect students who may require additional academic support. Model effectiveness is assessed using standard evaluation measures to ensure dependable performance. The outcomes of the analysis demonstrate that learning analytics provides actionable insights into student academic behaviour and enables early identification of performance-related risks. These insights can assist educators in implementing timely, focused, and personalised academic interventions. The study highlights the practical value of integrating learning analytics into smart education systems to strengthen instructional planning and institutional decision-making. By promoting data-informed educational practices, learning analytics contributes to improved academic achievement and supports the ongoing digital transformation of modern education systems.