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Artificial Intelligence in Education

Overview of attention for book
Cover of 'Artificial Intelligence in Education'

Table of Contents

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    Book Overview
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    Chapter 1 Investigating the Impact of a Meaningful Gamification-Based Intervention on Novice Programmers’ Achievement
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    Chapter 2 Automatic Item Generation Unleashed: An Evaluation of a Large-Scale Deployment of Item Models
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    Chapter 3 Quantifying Classroom Instructor Dynamics with Computer Vision
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    Chapter 4 Learning Cognitive Models Using Neural Networks
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    Chapter 5 Measuring the Quality of Assessment Using Questions Generated from the Semantic Web
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    Chapter 6 Balancing Human Efforts and Performance of Student Response Analyzer in Dialog-Based Tutors
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    Chapter 7 An Instructional Factors Analysis of an Online Logical Fallacy Tutoring System
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    Chapter 8 Using Physiological Synchrony as an Indicator of Collaboration Quality, Task Performance and Learning
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    Chapter 9 Towards Combined Network and Text Analytics of Student Discourse in Online Discussions
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    Chapter 10 How Should Knowledge Composed of Schemas be Represented in Order to Optimize Student Model Accuracy?
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    Chapter 11 Active Learning for Improving Machine Learning of Student Explanatory Essays
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    Chapter 12 Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms
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    Chapter 13 Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling
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    Chapter 14 Better Late Than Never but Never Late Is Better: Towards Reducing the Answer Response Time to Questions in an Online Learning Community
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    Chapter 15 Expert Feature-Engineering vs. Deep Neural Networks: Which Is Better for Sensor-Free Affect Detection?
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    Chapter 16 A Comparison of Tutoring Strategies for Recovering from a Failed Attempt During Faded Support
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    Chapter 17 Validating Revised Bloom’s Taxonomy Using Deep Knowledge Tracing
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    Chapter 18 Communication at Scale in a MOOC Using Predictive Engagement Analytics
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    Chapter 19 How to Use Simulation in the Design and Evaluation of Learning Environments with Self-directed Longer-Term Learners
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    Chapter 20 Students’ Academic Language Use When Constructing Scientific Explanations in an Intelligent Tutoring System
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    Chapter 21 Automated Pitch Convergence Improves Learning in a Social, Teachable Robot for Middle School Mathematics
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    Chapter 22 The Influence of Gender, Personality, Cognitive and Affective Student Engagement on Academic Engagement in Educational Virtual Worlds
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    Chapter 23 Metacognitive Scaffolding Amplifies the Effect of Learning by Teaching a Teachable Agent
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    Chapter 24 A Data-Driven Method for Helping Teachers Improve Feedback in Computer Programming Automated Tutors
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    Chapter 25 Student Agency and Game-Based Learning: A Study Comparing Low and High Agency
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    Chapter 26 Engaging with the Scenario: Affect and Facial Patterns from a Scenario-Based Intelligent Tutoring System
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    Chapter 27 Role of Socio-cultural Differences in Labeling Students’ Affective States
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    Chapter 28 Testing the Validity and Reliability of Intrinsic Motivation Inventory Subscales Within ASSISTments
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    Chapter 29 Correctness- and Confidence-Based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging
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    Chapter 30 Bring It on! Challenges Encountered While Building a Comprehensive Tutoring System Using ReaderBench
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    Chapter 31 Predicting the Temporal and Social Dynamics of Curiosity in Small Group Learning
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    Chapter 32 Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed?
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    Chapter 33 Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge
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    Chapter 34 Reciprocal Content Recommendation for Peer Learning Study Sessions
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    Chapter 35 The Impact of Data Quantity and Source on the Quality of Data-Driven Hints for Programming
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    Chapter 36 Predicting Question Quality Using Recurrent Neural Networks
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    Chapter 37 Sentence Level or Token Level Features for Automatic Short Answer Grading?: Use Both
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    Chapter 38 When Optimal Team Formation Is a Choice - Self-selection Versus Intelligent Team Formation Strategies in a Large Online Project-Based Course
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    Chapter 39 Perseverance Is Crucial for Learning. “OK! but Can I Take a Break?”
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    Chapter 40 Connecting the Dots Towards Collaborative AIED: Linking Group Makeup to Process to Learning
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    Chapter 41 Do Preschoolers ‘Game the System’? A Case Study of Children’s Intelligent (Mis)Use of a Teachable Agent Based Play-&-Learn Game in Mathematics
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    Chapter 42 Vygotsky Meets Backpropagation
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    Chapter 43 Providing Automated Real-Time Technical Feedback for Virtual Reality Based Surgical Training: Is the Simpler the Better?
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    Chapter 44 Reciprocal Kit-Building of Concept Map to Share Each Other’s Understanding as Preparation for Collaboration
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    Chapter 45 Early Identification of At-Risk Students Using Iterative Logistic Regression
Overall attention for this book and its chapters
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Mentioned by

1 news outlet
2 policy sources
9 X users
2 Wikipedia pages


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Readers on

27 Mendeley
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Artificial Intelligence in Education
Published by
Springer International Publishing, July 2018
DOI 10.1007/978-3-319-93843-1
978-3-31-993842-4, 978-3-31-993843-1

Penstein Rosé, Carolyn, Martínez-Maldonado, Roberto, Hoppe, H. Ulrich, Luckin, Rose, Mavrikis, Manolis, Porayska-Pomsta, Kaska, McLaren, Bruce, du Boulay, Benedict

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 11%
Student > Master 2 7%
Student > Bachelor 1 4%
Other 1 4%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 18 67%
Readers by discipline Count As %
Computer Science 4 15%
Business, Management and Accounting 2 7%
Psychology 1 4%
Materials Science 1 4%
Engineering 1 4%
Other 0 0%
Unknown 18 67%