The integration of artificial intelligence has been transforming education analytics and enhancing student learning continuously. Deep learning (DL) algorithms stand out as a particularly promising domain of technological advancement that could greatly enhance students’ educational experiences. In the pursuit of enhancing individualized education, the KNIGHT Multi-Model Learning Analytics system integrates an innovative Artificial Neural Network (ANN) model, leveraging data analytics to predict student performance with remarkable precision. This meticulously crafted model harnesses the power of data analytics and personalized learning models to gain valuable insights into student progress and provide targeted support. In addition, the user scenario exemplifies how educators leverage advanced analytics and customized learning models, providing personalized recommendations and support strategies to assist students in achieving academic success. This study purposefully concentrates on the early forecasting of student outcomes utilizing existing data, deliberately omitting the investigation of intervention tactics and their efficacy. Furthermore, the research does not take into account the varied educational goals and backgrounds of students, which may restrict the depth of understanding into their involvement and performance patterns. In summary, integrating deep learning methods into educational analytics is a significant advancement for enhancing student learning.