A BI approach for Student Engagement and Retention along with Cognitive Load analysis for Educator

Abstract

This research introduces a systematic approach to monitoring student engagement, retention, and cognitive load in higher education, combining Business Intelligence (BI) tools with cognitive load analysis to address the complex challenges educators face in understanding and supporting student learning. By integrating diverse data sources, such as attendance, academic performance, mental health indicators, demographic factors, and feedback, the study demonstrates how a data-driven framework can provide educators with real-time, actionable insights into student behavior patterns. The BI system revealed valuable trends, including attendance irregularities, declining grades, and demographic influences, which helped educators identify at-risk students early and respond proactively. Additionally, the research employed cognitive load analysis to evaluate the mental demands of course content, categorizing learning objectives according to Bloom’s Taxonomy. This analysis helped educators identify content that may overwhelm students and adjust complexity levels to support effective learning. The combined use of BI insights and cognitive load data presented a holistic approach that allowed educators to monitor student engagement more effectively and tailor instructional content to meet students' needs without cognitive overload. Findings from this study suggest that integrating BI tools with cognitive load metrics provides a structured, insightful framework that not only facilitates proactive intervention for student retention but also supports educators in creating a balanced, engaging, and supportive learning environment. This research offers a practical model that empowers institutions to foster student success through data-driven instructional adjustments and comprehensive engagement monitoring, meeting the evolving demands of modern higher education