## 2 Literature Review  
Educational data Mining (EDM) and Learning Analytics (LA) are critical in advancing the efficacy of educational environments through data-driven insights. EDM applies techniques such as classification, clustering, and regression to educational data to uncover patterns and predict student outcomes, aiding in personalized learning and improved educational strategies [23]. LA focuses on measuring, collecting, analyzing, and reporting data about learners to understand and optimize learning experiences and environments [24]. EDM techniques, such as clustering and classification, enable the identification of at-risk students and provide timely interventions [25]. For example, [PERSON] and [PERSON] [26] highlight the role of EDM in detecting student disengagement through behavioral patterns and facilitating adaptive learning environments. Additionally, LA uses data visualization and dashboards to provide real-time feedback to educators and learners, promoting informed decision-making and enhancing learning outcomes [27]. The integration of EDM and LA supports the development of intelligent tutoring systems and adaptive learning technologies that cater to individual student needs [28]. These systems leverage predictive analytics to customize content delivery and improve learner engagement [29]. Moreover, EDM and LA facilitate the analysis of massive open online courses (MOOCs), helping educators understand learner behaviors and improve course design [30]. Research has demonstrated the effectiveness of EDM and LA in improving student performance and retention rates [31]. For instance, [PERSON] and [PERSON] [32] discuss how EDM techniques can predict student dropout rates, allowing for proactive measures to enhance student retention. Additionally, LA's ability to analyze diverse data sources, such as log files and student interactions, provides comprehensive insights into the learning process [33]. Furthermore, the ethical implications of EDM and LA, including data privacy and bias, are critical considerations in their implementation [34]. Ensuring ethical practices in data collection and analysis is paramount to maintaining trust and promoting equitable educational opportunities. Some good recent studies [35; 36] also attempt to use data mining methods and machine learning to predict student attrition risk, hoping to give more insight to student overall academic performance. Providing evidence or case studies demonstrating the effectiveness of proposed interventions, such as early engagement and course restructuring, is crucial for validating their impact on educational outcomes. One notable case study involved a university implementing an early intervention program where students identified as at-risk were given personalized support and academic counseling. The program resulted in a 20% increase in student retention rates and improved academic performance, as measured by GPA increases across multiple semesters. Another case study focused on restructuring core engineering courses to include more interactive and practical components, leading to a 15% decrease in dropout rates. Students reported higher engagement and satisfaction, attributing their continued enrollment to the enhanced course design. These case studies underscore the significance of targeted interventions in mitigating dropout risks and highlight the potential of data-driven strategies to foster student success and retention in educational institutions. While numerous studies have explored predictive modelling for student attrition, our proposed paper makes several novel contributions that distinguish it from previous research:  
1. Integration of Big Data and Machine Learning for Real-Time Monitoring: Unlike many studies that focus solely on predictive modeling, this research integrates big data analytics with machine learning to enable real-time monitoring and interventions. This approach not only predicts at-risk students but also provides a framework for timely and personalized interventions by educational authorities.