Artificial Intelligence Supported Educational Technologies

✍ Scribed by Niels Pinkwart (editor), Sannyuya Liu (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 299
• Series: Advances in Analytics for Learning and Teaching
• Edition: 1
• Category: Library

✦ Synopsis

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.

✦ Table of Contents

Preface
The Symposium of Sino-German Perspective on AI-Driven Educational Technology
This Book
Contents
Part I: Overviews
Open Learning Analytics: A Systematic Literature Review and Future Perspectives • Arham Muslim, Mohamed Amine Chatti, and Mouadh Guesmi
1 Introduction
2 Open Learning Analytics
3 Openness in Current LA Tools
3.1 Methodology
3.2 Template Analysis
3.2.1 Data, Environments, Context (What?)
Data Environments
Data Types
Data Models
3.2.2 Stakeholders (Who?)
3.2.3 Objectives (Why?)
3.2.4 Methods (How?)
Analysis Types
Visualization Settings
3.3 Summary
4 A Comparison of OLA Frameworks
4.1 Society for Learning Analytics Research
4.2 Apereo Learning Analytics Initiative
4.3 Jisc Open Learning Analytics Architecture
4.4 SURFnet Learning Analytics
4.5 Open Learning Analytics Platform
4.6 Comparison
4.6.1 Data Models and Specifications (What?)
4.6.2 Stakeholders and Objectives (Who? and Why)
4.6.3 Methods (How?)
5 OLA Platform Requirements
6 OLA Challenges and Future Perspectives
6.1 Technical Aspects
6.2 Pedagogical Aspects
6.2.1 Adoption
6.2.2 Action Support
6.2.3 Human-Centered Open Learning Analytics
7 Conclusion
References
Non-distracting Feedback in Artificial Intelligence Supported Learning • Matthias Wölfel
1 Introduction
2 Sensor Information in Artificial Intelligence Supported Learning
3 Time of Information
3.1 Problems of Immediate or Fixed-Duration Feedback
3.2 Timing Feedback
4 Amount of Information
4.1 Problems with Information Overflow
4.2 Gesture-Based Learning
5 Representation of Information
5.1 Problems with Direct Representation and Comparison of Information
5.2 Metaphoric Visualization
5.3 Representation of Uncertainty
6 Conclusion, Limitations, and Outlook
References
Research on Human-Computer Cooperative Teaching Supported by Artificial Intelligence Robot Assistant • Fang Haiguang, Wang Shichong, Xue Shushu, and Wang Xianli
1 Introduction
2 Key Technology of Artificial Intelligence Robot Assistant
3 Collaborative Teaching Environment Based on Artificial Intelligence Robot Assistant
4 Analysis of Collaborative Teaching Process Based on Artificial Intelligence Robot Assistant
5 Collaborative Teaching Design Based on Artificial Intelligence Robot Assistant
6 Collaborative Teaching Case Based on the Artificial Intelligence Robot Assistant
7 Conclusion and Discussion
References
A New Conceptual Framework for Measuring Online Listening in Asynchronous Discussion Forums • Huanyou Chai, Zhi Liu, Tianhui Hu, and Qing Li
1 Introduction
2 Terminology
2.1 Online Listening
2.2 Reading
2.3 Lurking
3 Measurement of Online Listening
4 A Conceptual Framework for Measuring Online Listening
4.1 Redesign of Asynchronous Discussion Forum
4.2 Methods of Data Analysis
4.3 Conceptual Framework
5 Discussion
5.1 Contribution to Knowledge
5.2 Future Research
References
Part II: Systems
Self-Improvable, Self-Improving, and Self-Improvability Adaptive Instructional System • Zhou Long, Frank Andrasik, Kai Liu, and Xiangen Hu
1 Introduction
2 Background
2.1 Advanced Personalized Learning
2.2 Intelligent Tutoring Systems
3 Self-Improvable Adaptive Instructional System
3.1 Four-Component Model of AIS
3.2 Self-Improvable AIS in Today’s Context
4 A Learner-Resource Symmetric Model of SIAIS
4.1 Observed Symmetry in Item Response Theory
4.2 A Learner-Based-Symmetric Self-Improvable Learning Resource
4.3 An Example of a Self-Improvable ITS
5 Conclusion
References
Can Sensors Effectively Support Learning? • Albrecht Fortenbacher and Haeseon Yun
1 Introduction
2 A Sensor Device for Learning
3 Sensor Data Analysis and Emotion Prediction
4 A Sensor-Based Learning Companion
5 Distributed Learning Analytics
6 Privacy and Trust
7 Conclusion and Outlook
References
A Prototype System of Search: Finding Short Material for Science Education in Long and High-Definition Documentary Videos • Tai Wang, Yu-chen Liu, Zhi Liu, Ming Zhang, Jiao Liu, and Ya-mei Zhu
1 Introduction
2 Literature Review
2.1 Concept Extraction and Organization
2.2 Video Tagging
2.3 Search Results Re-ranking
3 System Framework
4 Components
4.1 Knowledge Map Extraction
4.2 Documentary Subtitle Tagging
4.3 Hit Re-ranking
5 Preliminary Results
5.1 Experts Scoring on Final Search Results (FSR)
6 Conclusion and Future Work
References
A Learning Attention Monitoring System via Photoplethysmogram Using Wearable Wrist Devices • Qing Li, Yuan Ren, Tianyu Wei, Chengcheng Wang, Zhi Liu, and Jieyu Yue
1 Introduction
2 Background
2.1 Learning Attention
2.2 Methods of Monitoring Learning Attention
3 Methodology
3.1 Experimental Procedure
3.2 Data Acquisition
3.3 Data Analysis
4 Results
5 Learning Attention Monitoring System
6 Conclusion and Future Work
References
Part III: Algorithms
Toward Improving Social Interaction Ability for Children with Autism Spectrum Disorder Using Social Signals • Jingying Chen, Guangshuai Wang, Kun Zhang, Ruyi Xu, Dan Chen, and Xiaoli Li
1 Introduction
2 Related Work
3 System Architecture
4 Visual Inputs Process
4.1 Attention Detection
4.2 Expression Recognition and Intensity Estimation
4.3 Multicamera Surveillance
5 Experiments and Results
5.1 Attention Detection
5.2 Expression Recognition
5.3 Studies of Engagement Support
6 Conclusion
References
Personalized Citation Recommendation Using an Ensemble Model of DSSM and Bibliographic Information • Wael Alkhatib and Christoph Rensing
1 Introduction
2 Related Work
3 Methodology
3.1 Ontology Construction
3.2 Query-Based Recommendation Module
3.3 Graph-Based Ranking Modules
3.4 Ranking Module
4 Dataset and Evaluation Settings
5 Evaluation Results
5.1 Q-DSSM Structure Analysis
5.2 Personalized Versus Non-personalized Recommendation
5.3 Comparison with Other Personalized Citation Recommendation Systems
6 Conclusion
References
Augmented: Academic Performance Prediction Based on Digital Campus • Liang Zhao, Kun Chen, Zhi Liu, Jie Song, Xiaoliang Zhu, Ming Xiao, Brian Caulfield, and Brian Mac Namee
1 Introduction
2 Problem Formulation
2.1 Raw Dataset
2.2 Privacy Protection
3 Augmented Framework
3.1 Data Module
3.2 Prediction Module
3.3 Visualization Module
4 Experimental Results
4.1 Results of Prediction
4.2 Identify At-Risk Students by the Prediction
5 Conclusion and Future Work
References
Joint Embedding Learning of Educational Knowledge Graphs • Siyu Yao, Ruijie Wang, Shen Sun, Derui Bu, and Jun Liu
1 Introduction
2 Related Work
2.1 KG Embedding Techniques
2.2 Literal Representation Techniques
3 The Proposed Model
3.1 Problem Formulation
3.2 Overview of the Model
3.3 Structural Embedding Learning
3.4 Literal Embedding Computing
3.5 Joint Embedding Learning
4 Experiments
4.1 Educational KG Construction for Experiments
4.2 Link Prediction Over Knowledge Forest
4.3 Effectiveness Evaluation Over Common Benchmarks
5 Conclusions
References
Part IV: Empirical Studies
Modeling the Self-Regulated Learning Behaviors of Graduate Students in Online Academic Reading and Writing Environments • Hercy N. H. Cheng and Xiaotong Zhang
1 Introduction
2 Self-Regulated Learning Behavior Analysis
2.1 Lag Sequential Analysis
2.2 Sequential Pattern Mining and Differential Sequence Mining
2.3 Hidden Markov Models
3 Case 1: Negotiated Academic Reading Assessment
3.1 Design
3.2 Method
3.3 Results
4 Case 2: Online Academic Writing System
4.1 Design
4.2 Method
5 Results
6 Concluding Remarks
References
Mapping Machine-Generated Questions to Their Related Paragraphs in the Textbook • Lishan Zhang
1 Introduction
2 The Machine-Generated Questions
3 The Mapping Task
3.1 Task Definition
3.2 Keyword Generation
3.3 Task Reformation
4 Methodology
4.1 Preprocessing
4.2 Question-Paragraph Mapping
5 Evaluation
5.1 Measures
5.2 Results
5.3 Discussion
6 Related Works
6.1 Document Retrieval
6.2 Question Answering
7 Conclusion
References
Change Management for Learning Analytics • Dirk Ifenthaler
1 Introduction
2 Readiness for Learning Analytics
3 Case Study 1: Stakeholders Perspectives on Change
3.1 Method
3.2 Results
4 Case Study 2: Embedding Learning Analytics into an Existing Legacy Environment of a University
4.1 Concept of Implementation
4.2 Dealing with Data Privacy Requirements
5 Discussion and Conclusion
References
Lessons Learned from Designing Adaptive Training Systems • Ina Müller, Tobias Moebert, and Ulrike Lucke
1 The Promise and Pitfalls of Personalization
2 Fundamentals of Emotion-Sensitive Systems
3 EVA: An Adaptive Training System for People with Autism
4 Ethical Reflections and Implications
5 Conclusion and Recommendations
References
Index