Intelligence Quotient insights through pattern mining Leveraging Tensor Factorization Techniques for personalized Education and Adaptive Learning Systems

Intelligence Quotient (IQ) insights gained through data-driven methods are vital for improving learning experiences in the age of personalized education. This study presents IntelliTensorNet, a new framework for improving adaptive learning systems through pattern analysis of student performance. The framework uses Tensor Factorisation (HOSVD) and Neural Collaborative Filtering (NCF). A multiple dimensions tensor description of educational data, including educational scores, academic routines, and activities outside of school, will be created as part of the suggested approach. Latent intelligence characteristics are obtained via HOSVD, while individualized learning suggestions are predicted using NCF, considering both previous achievement and mental processes.IntelliTensorNet demonstrates an 18% enhancement in the accuracy of learning outcome predictions compared to traditional matrix-based methods. The framework facilitates the creation of personalized study plans by precisely identifying key factors that influence student performance.IntelliTensorNet provides a data-driven and scalable approach to personalized learning that can illuminate students' intelligence and enhance their academic performance.