An Educational Human Digital Twin Proposed Model for Personalized E-Learning

Abstract

Global education systems are shifting from traditional one-size-fits-all learning approaches towards a more personalized, studentcentered approach so that students can progress at their own pace. Several personalized learning techniques were used, however, these techniques depended mainly on historical data. This research proposes a framework for real-time based personalized learning system. The system depends on real time data and digital twins to provide a more effective personalized learning experience for higher education students. Depending on real-time data about students is expected to provide a more effective personalized learning experience. In addition, it is expected to make the personalized learning process more adaptive. This research states the results of phase 1 of a financed project that aims to build a prototype of a student digital twin for personalized learning. The project is also extended to the development of academic staff members' knowledge, skills and study material improvements. However, this research discusses these points briefly and focuses on the discussion of students personalized learning. In this research the appropriate attributes for building a student digital twin, and the suitable tools for capturing the realtime data of students are determined. In addition, the architecture of the digital twin based personalized learning system, the real-time personalized learning process, the potential insights and decisions are discussed. As for future work, the project continues to the modelling of algorithms for the student digital twin and actual building of the digital twin. It is then extended to setting and using the tools and testing the results.

Topics & Concepts