AI-Enabled Learning Engagement Analysis
Description
Artificial Intelligence (AI) is revolutionizing the education industry, particularly in understanding and enhancing student learning engagement in online learning. The book aims to get an in-depth exploration into a three-stage framework of AI-enabled learning engagement analysis, which provides effective tools and methodologies for education researchers and practitioners, facilitating intelligent transformation and optimization. It delves into intelligent recognition, including emotional and cognitive engagement, supported by detailed algorithmic introductions and case studies. The book further examines the relationship between engagement and outcomes, covering cognition, emotion, self-regulation, and social network characteristics. Furthermore, it presents adaptive interventions, offering a comprehensive guide to leveraging AI for personalized learning experiences and improved learning outcomes.
The book provides valuable insights and practical tools for enhancing teaching and learning outcomes. Whether you are an educator seeking to integrate AI into your teaching strategies or a researcher exploring the frontiers of educational technology, this book equips you with the knowledge and resources to navigate the evolving landscape of AI-enabled education.
This publication underscores the significance of practical applications, presenting a compendium of pioneering research methodologies and case studies that elucidate the integration of AI and data analytics within pedagogical contexts. Whether the objective is to refine comprehension of online learning engagement dynamics or to deploy AI-infused strategies in educational settings, this text serves as an invaluable resource for revolutionizing education with AI.
Chapter 2 is available for open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
More details
Other editions
Additional editions
Persons
Zhi Liu is the PI of affective computing research group, associate researcher and doctoral supervisor at the National Engineering Research Center of Educational Big Data, Central China Normal University. His focuses include Educational Psychology, Learning Analytics and Educational Data Mining. He was a guest researcher at the Institute of Computer Science of Humboldt University of Berlin from 2017 to 2018. He is an expert for National Graduate Education Evaluation and Monitoring, an evaluation expert of the National Natural Science Foundation and an evaluation expert for Hubei Provincial Department of Science and Technology, and the leader of the National Science and Technology Innovation 2030 Major Project and National Natural Science Foundation.
Yao Xiao
is a research assistant at the National Engineering Research Center for Big Data Education, Central China Normal University. He graduated from Wollongong Joint Research Institute of Central China Normal University with a master's degree in Computer science. His research interests include personalized conversation generation, data mining, and natural language processing.
Content
Chapter 1 Overview of Learning Engagement Research.- Chapter 2 Literature Review of Learning Engagement Research.- Chapter 3 Intelligent Recognition of Learning Engagement.- Chapter 4 Relationship Between Learning Engagement and Outcomes.- Chapter 5 Adaptive Interventions in Learning Engagement.- Chapter 6 Prospects and Challenges in AI-Enabled Learning Engagement.