New Learner Model for Intelligent And Adaptive E-learning System

In recent years, the use of E-learning systems has increased and their efficiency has been improved, especially after emergence events like the COVID-19 crisis and other emergency situations like earthquakes in Turkey and Syria. E-learning applications present a solution for the continuity of the learning process. Learner models have been intensively applied in the learning system to improve online learning. In order to achieve this objective, it is imperative to use adaptive learning systems because learners do not have the same mental capacities and similar physical characteristics. In fact, each learner has individual features such as knowledge, goals, experiences, interests, backgrounds, personal traits, learning styles, learning activities, and study results. To make the learning content available for all learners, facilitate the learning process, and meet the learner's needs, an adaptive learning system was introduced as an alternative to the standard and traditional "one-size-fits-all". The learner model is the pivot of any adaptive learning environment, which permits the improvement of the learning process. This paper introduces a new learner model including 2 dimensions: (1) static dimensions (demographic information, interests, background, experience, learning style preference and learning situation), and (2) dynamic dimensions (Learning materials, emotion, motivation, engagement, and interaction). The developed model is applied to improve the learning process and enhance the learning results in any learning situation. Our approach is a combination of many methods and techniques to initialize and update the learner model for learning personalization. The main objective of our paper is to develop a learner modeling architecture and the way that is used to provide an adaptive learning process.