Challenges and Strategies for Integrating Quantum Computing into Learning Analytics

Quantum computing (QC) is rapidly emerging as a transformative technology capable of significantly advancing learning analytics (LA). This paper investigates how QC can effectively address the challenges associated with the processing and analysis of complex educational datasets. Explore applications of quantum methods in personalized recommendation systems, student performance prediction, educational content optimization, and adaptive learning platforms. In addition, the study examines critical integration issues—including compatibility between classical and quantum frameworks, algorithmic complexity, and performance evaluation. By highlighting both advantages and limitations of quantum approaches, this research offers insight into how QC can complement and enhance current LA practices, ultimately contributing to the development of more precise, efficient, and personalized educational analytics tools. These insights may serve as a foundation for future research and practical deployments of quantum-enhanced educational analytics.