Adaptive Learning for Complex Thinking: A Systematic Review of Users' Profiling Strategies

Abstract

Adaptive learning strategies applied to e-learning have become a relevant approach towards diversity and inclusion. They bring several benefits to learners in the role of digital platform users, related to the user experience, but primarily in the learning process optimization. It aims to provide mediation, tailored content, and adequate channels for users' capabilities and learning styles. The OpenEdR4C is a digital open educational platform designed to expand complex reasoning skills in students and lifelong learners of higher education. The platform addresses five types of learning obstacles: sensory limitations, learning styles, sociodemographic and socioeconomic contexts, and certain kinds of neurodiversity. All these considerations require a dynamic and assertive user profiling strategy to provide compelling adaptive learning experiences. This paper presents a Systematic Literature Review of user profiling strategies published in the last five years in SCOPUS and Web of Science databases. The findings allowed for the identification of successful and applicable strategies that the OpenEdR4C research and development teams used to select and shape the suitable strategy for the platform in three levels: a) procedures that allow the user to self-declare their profile and preferences; b) profiling based on the system's detection of patterns and behaviors shown by the users; and c) evaluation techniques to validate the effectiveness of the profiling instruments. The results and discussion presented are valuable insights for educators, developers in the context of open educational resources design, and decision-makers of HiEd institutions or training centers. There is a suitable strategy for every type of profiling necessity; here is a combination of many to be used and developed collectively.