Detailed Study of Learning Behavior Analysis Approaches to Tailor Educational Experiences Based on Individual Preferences

Abstract

Over the past couple of years, it has been
increasingly stressed that the traditional, standardized methods
of educating people should be replaced with personalized forms
of learning that suits a wide range of individual needs and
requirements. The increasing availability of considerable
amounts of data on learner interactions, combined with the
advancements in artificial intelligence, machine learning, and
educational technologies, has largely prompted this shift.
Behavior Analysis as a study (LBA) has become one of the key
methods of studying the learning processes of learners and their
interactions with educational tools, materials, and content. LBA
allows the creation of adaptive systems that change instruction
strategies and content delivery dynamically through an analysis
of information on behaviors, cognitive processes, and affect. In
this survey paper, an overview of the underlying principles,
techniques, and technologies of learning behavior analysis will
be offered. It investigates multiple options such as the rule-based
systems, supervised and unsupervised learning models,
reinforcement learning, and educational data mining
approaches. Also, the paper discusses how these analytical
methods can aid the personalization of the educational
experiences by use of the learner profiling protocol, adaptive
learning systems, intelligent tutoring environment, and realtime
feedback systems. This study reminds us how powerful
behavior-driven personalization can become by indicating the
existing trends, challenges, and future trends that help learners
better interact, perform, and like learning in different
environments.