Intelligent Systems and Learning Data Analytics in Online Education
- 1st Edition - June 15, 2021
- Editors: Santi Caballé, Stavros N. Demetriadis, Eduardo Gómez-Sánchez, Pantelis M. Papadopoulos, Armin Weinberger
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 4 1 0 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 1 2 7 - 2
Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning.… Read more
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Request a sales quoteIntelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general.
This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply.
- Presents the application of innovative AI techniques to collaborative learning activities
- Offers strategies to provide automatic and effective tutoring to students’ activities
- Offers methods to collect, analyze and correctly visualize learning data in educational environments
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Foreword
- Preface
- Final words
- Acknowledgments
- Conversational agents in MOOCs: reflections on first outcomes of the colMOOC project
- 1 Introduction
- 2 Background
- 3 The colMOOC project
- 4 “Computational Thinking” MOOC
- 5 “Programming for non-programmers” MOOC
- 6 “Educational technology for the classroom” MOOC
- 7 “Educational technologies in support for collaboration and evaluation in virtual learning environments” MOOC
- 8 Discussion and conclusions
- Acknowledgments and disclaimer
- Further reading
- Part I: Intelligent Agent Systems and Learning Data Analytics
- Chapter 1. Artificial Intelligence (AI)-enabled remote learning and teaching using Pedagogical Conversational Agents and Learning Analytics
- Abstract
- 1.1 Introduction
- 1.2 Literature review
- 1.3 Our experience with Pedagogical Conversational Agents
- 1.4 Results and analysis
- 1.5 Discussion
- 1.6 Future trends and conclusion
- References
- Chapter 2. Integrating a conversational pedagogical agent into the instructional activities of a Massive Open Online Course
- Abstract
- 2.1 Introduction
- 2.2 Literature review
- 2.3 Research methodology
- 2.4 Results and discussion
- 2.5 Conclusions and future work
- References
- Chapter 3. Improving MOOCs experience using Learning Analytics and Intelligent Conversational Agent
- Abstract
- 3.1 Introduction
- 3.2 Online learning and MOOCs
- 3.3 Methodology
- 3.4 LAICA integration in MOOCs: framework
- 3.5 LAICA integration in MOOCs: example implementation
- 3.6 LAICA integration in MOOC: impact analysis
- 3.7 LAICA integration in MOOC: findings and discussion
- 3.8 Conclusion
- References
- Further reading
- Chapter 4. Sequential engagement-based online learning analytics and prediction
- Abstract
- 4.1 Introduction
- 4.2 Related work
- 4.3 Sequential engagement-based academic performance prediction network
- 4.4 Experiments and evaluation
- 4.5 Conclusions and future work
- Acknowledgment
- References
- Chapter 5. An intelligent system to support planning interactive learning segments in online education
- Abstract
- 5.1 Introduction
- 5.2 Theoretical backgrounds of intelligent systems in active learning systems
- 5.3 Methodology of implementation and target group
- 5.4 Results and discussion
- 5.5 Conclusion
- References
- Part II: Artificial Intelligence Systems in Online Education
- Chapter 6. A literature review on artificial intelligence and ethics in online learning
- Abstract
- 6.1 Introduction and motivations
- 6.2 Artificial intelligence in online learning
- 6.3 Ethics in online learning
- 6.4 Ethics in artificial intelligence
- 6.5 Limitations of ethics by design and how moral systems can overcome them
- 6.6 Reflections and guidelines for an ethical use of artificial intelligence in online learning
- 6.7 Concluding remarks and future work
- Acknowledgments
- References
- Chapter 7. Transfer learning techniques for cross-domain analysis of posts in massive educational forums
- Abstract
- 7.1 Introduction
- 7.2 Related works
- 7.3 Text categorization model
- 7.4 Transfer learning strategy
- 7.5 Experiments and evaluation
- 7.6 Conclusions and further work
- References
- Chapter 8. Assisted education: Using predictive model to avoid school dropout in e-learning systems
- Abstract
- 8.1 Introduction
- 8.2 Background
- 8.3 Related work
- 8.4 PRIOR Ensemble Architecture
- 8.5 DPE-PRIOR: Dropout predictive Ensemble Model
- 8.6 Final remarks
- Acknowledgment
- References
- Chapter 9. Adaptive task selection in automated educational software: a comparative study
- Abstract
- 9.1 Introduction
- 9.2 The pedagogical implications of personalized learning systems
- 9.3 Overview of the different adaptation schemes
- 9.4 Methodology and research approach
- 9.5 Simulation results
- 9.6 Discussion
- 9.7 Conclusions
- References
- Appendix A
- Chapter 10. Actor’s knowledge massive identification in the learning management system
- Abstract
- 10.1 Introduction
- 10.2 Diagnosis of higher education in Morocco and contribution of e-learning
- 10.3 Machine learning evaluation tools
- 10.4 E-learning massive data
- 10.5 Conclusion
- References
- Part III: Applications of Intelligent Systems for Online Education
- Chapter 11. Assessing students’ social and emotional competencies through graph analysis of emotional-enriched sociograms
- Abstract
- 11.1 Introduction
- 11.2 State-of-the-art analysis
- 11.3 Socioemotional graph analysis
- 11.4 Application domains
- 11.5 Conclusions and open research areas
- References
- Chapter 12. An intelligent distance learning framework: assessment-driven approach
- Abstract
- 12.1 Introduction
- 12.2 Sketch of intelligent distance teaching and learning framework
- 12.3 Three-layered distance teaching and learning platform
- 12.4 Learning assessment and feedback
- 12.5 Analytics: mining from learning graph and assessment graph
- 12.6 Simulation result
- 12.7 Concluding remarks
- References
- Chapter 13. Personalizing alternatives for diverse learner groups: readability tools
- Abstract
- 13.1 Introduction
- 13.2 Personalization in online education: trends, systems, and approaches
- 13.3 Implementing readability tools: general steps and suggestions
- 13.4 Discussion
- 13.5 Conclusion
- References
- Chapter 14. Human computation for learning and teaching or collaborative tracking of learners’ misconceptions
- Abstract
- 14.1 Introduction
- 14.2 Related work
- 14.3 Results from preliminary studies
- 14.4 System architecture
- 14.5 Conclusion
- References
- Index
- No. of pages: 424
- Language: English
- Edition: 1
- Published: June 15, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128234105
- eBook ISBN: 9780128231272
SC
Santi Caballé
SD
Stavros N. Demetriadis
EG
Eduardo Gómez-Sánchez
PP
Pantelis M. Papadopoulos
AW