Student Modeling and Analysis in Adaptive Instructional Systems

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, personalize student education. A necessary part of any such system model used predict or analyze learner behavior inform adaptation. To help researchers this area, paper presents state-of-the-art review 11 years research (2010-2021) modeling, focusing on characteristics, learning indicators, foundational aspects dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional data create multimodal models real-world systems. In addition, we discuss challenges inherent as uncontrolled collection environments leading noisy sync issues. Finally, reinforce our findings conclusions through an industry case study system. study, verify that adding multiple modalities increases from 53.3% 69%. At the same time, encountered with including environment inevitably data, calls for synchronization noise control strategies quality usability.