A Systematic Review on Assessment in Adaptive Learning: Theories, Algorithms and Techniques

Abstract

Computerized knowledge assessments have become increasingly popular, especially since COVID-19 has transformed assessment practices from both technological and pedagogical standpoints. This systematic review of the literature aims to analyze studies concerning the integration of adaptive assessment techniques and algorithms in Learning Management Systems (LMS) to generate a global vision of their potential to enhance the quality and adaptability of learning, and to provide recommendations for their application. A review of international indexed databases, specifically Scopus, was conducted, focusing on studies published between 2000 and 2024. The PICO framework was used to formulate the search query and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to select 66 relevant studies based on inclusion and exclusion criteria such as publishing year, document type, subject area, language, and other factors. The results reveal that integrating adaptive assessments positively impacts the quality of learning by generating short tests dynamically adapted to students’ skills, learning styles, and behaviors. Furthermore, the findings identify various techniques and algorithms used, as well as their main features and benefits. These tools tailor adaptive learning programs to meet students’ specific needs, preferences, and proficiency levels, thereby enhancing student motivation and enabling them to engage with material that matches their knowledge and abilities. In conclusion, the systematic review emphasizes the significance of integrating adaptive assessments in educational environments and offers tailored recommendations for their implementation to provide adaptive learning. These recommendations can be adopted and reused as guidelines to develop new and more sophisticated assessment models.