Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach

Abstract

The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digital technology, the integration of education technology in the classroom has led to a change in teaching methods and learning strategies. In this paper, we introduce the KNIGHT (AI in Education at Hochschule für Technik (HFT) Stuttgart) framework, which is a holistic solution designed to tackle the complex issue of personalized education in a digital era. The paper explores the application of multimodal data integration, the novel application of deep reinforcement learning to education analytics, and the ethical consideration of privacy-preserving personalized feedback. The proposed framework’s efficacy is substantiated through a case study, demonstrating its potential to revolutionize personalized education. This paper provides a comprehensive overview of the current discourse, providing valuable insights for educators, policymakers, and researchers into the multifaceted landscape of modern education, contributing to ongoing discussions and advancements in educational technology.

Keywords

• Artificial intelligence (AI)
• machine learning (ML)
• learning analytics (LA)
• multi-modal learning analytics (MMLA)
• deep learning (DL)
• deep reinforcement learning (DRL)