Integrating Deep Learning into Educational Big Data Analytics for Enhanced Intelligent Learning Platforms

Abstract

Exploring the field of educational big data analytics and gaining insights into student behaviour and its connection to academic performance is crucial for creating intelligent learning environments. Technological innovations have changed how students learn and reshaped the nature of education. Technological advancements have unquestionably made learning more accessible, faster, and enjoyable for pupils. When deep learning is integrated with learning management systems, intelligent course content may be generated with high accuracy, and no human interaction is required. This study utilises advanced deep learning techniques to analyse the xAPI-Educational Mining Dataset and reveal valuable insights that can significantly improve online learning experiences. The study underscores the crucial importance of parental involvement, emphasising its link to student attendance and overall satisfaction with the educational institution. In addition, the results suggest that students who actively participate in course announcements and utilise resources tend to achieve better academic outcomes, highlighting the significance of resource utilisation in achieving academic success. On the other hand, engaging in conversations seems to have a minimal effect on how students are categorised. Building upon these findings, a novel predictive model is introduced, utilising Long Short-Term Memory (LSTM) networks. This model utilises sequential student interaction data to predict future behaviour and academic outcomes, helping online platforms understand student actions and make informed decisions. This study makes a valuable contribution to developing cutting-edge intelligent learning approaches. It achieves this by utilising the potential of educational big data analytics and deep learning techniques.