Details

Subject(s)

• Artificial intelligence [Browse]
• Educational technology [Browse]

Related name

• Wilson, Joshua [Browse]

Library of Congress genre(s)

• Essays [Browse]

Series

Routledge International Handbooks Series [More in this series]

Summary note

This is a definitive guide at the intersection of automation, artificial intelligence, and education. This volume encapsulates the ongoing advancement of AEE, reflecting its application in both large-scale and classroom-based assessments to support teaching and learning endeavours.

Source of description

• Description based on publisher supplied metadata and other sources.
• Part of the metadata in this record was created by AI, based on the text of the resource.

Contents

• Cover
• Half Title
• Series Information
• Title Page
• Copyright Page
• Table of Contents
• About the Editors
• List of Contributors
• Foreword
• Acknowledgments
• Reviewer Acknowledgments
• Section 1 Introduction to AEE and Modern AEE Systems
• 1 Introduction to Automated Essay Evaluation
• 1.1 Introduction
• 1.2 The Evolution of Automated Scoring and Automated Feedback On Writing
• 1.2.1 The 2012 Hewlett Trials and Their Outcomes
• 1.2.2 The National Assessment of Educational Progress (NAEP) Trials
• 1.3 Current Use Cases for Automated Essay Evaluation
• 1.3.1 Evaluating Essays With 150 Words Or More
• 1.3.2 Short-Form Constructed Responses With Fewer Than 150 Words
• 1.3.3 Content-Intensive Responses
• 1.3.4 Content-Superficial Responses
• 1.3.5 Summatively Scored Essays
• 1.3.6 Formative Assessment
• 1.4 Frameworks for Validating AEE
• 1.5 Lingering and New Concerns Related to AEE
• 1.6 The Current Handbook: Apprising the State of the Art and Fostering Future Development
• References
• 2 Automated Essay Evaluation at Scale: Hybrid Automated Scoring/Hand Scoring in the Summative Assessment Program
• 2.1 Introduction
• 2.2 Progressive Hybrid Scoring Approaches
• 2.2.1 Overview
• 2.2.2 Project Essay Grade
• 2.2.2.1 PEG Architecture
• 2.2.2.2 PEG Hybrid Scoring Applications
• 2.2.2.3 Evidence for Use
• 2.2.3 Requirements
• 2.2.3.1 Training Data
• 2.2.3.2 Validation
• 2.2.4 Training
• 2.2.5 Hybrid Scoring Process
• 2.2.5.1 Role of Humans
• 2.2.5.2 Role of the Engine
• 2.3 Implications
• 2.3.1 Future Directions
• Notes
• 3 Exploration of the Stacking Ensemble Learning Algorithm for Automated Scoring of Constructed-Response Items in Reading Assessment
• 3.1 Introduction
• 3.2 Methods
• 3.2.1 Data
• 3.2.2 Model Building Process
• 3.2.2.1 Text Preprocessing and Processing.
• 3.2.2.2 Feature Extraction
• 3.2.2.3 Automated Scoring Classifier Development
• 3.2.3 Model Evaluation
• 3.3 Results
• 3.3.1 Automated Scoring Classifier Development
• 3.4 Summary and Discussion
• 4 Scoring Essays Written in Persian Using a Transformer-Based Model: Implications for Multilingual AES
• 4.1 Introduction
• 4.1.1 Persian as a Unique Case Study for Multilingual AES
• 4.1.2 Purpose of the Chapter
• 4.2 Overview of a Transformer-Based System for AES
• 4.2.1 Introduction to Transformers
• 4.2.2 Bidirectional Encoder Representations From Transformers
• 4.2.3 Multilingual BERT
• 4.3 Scoring Persian Essays Using MBERT Transformer Model
• 4.3.1 Data Set
• 4.3.2 Model Architecture
• 4.3.2.1 Word Embedding Word2Vec Model
• 4.3.2.2 Transformer MBERT Model
• 4.3.2.3 Hyperparameter Tuning
• 4.3.3 Performance Measures
• 4.4 Comparing the Performance of the Word Embedding and Transformer Models
• 4.4.1 Performance of Models Overall
• 4.4.2 Performance of Models By Score Level
• 4.5 Conclusions and Implications for Multilingual AES
• 4.5.1 The Importance of Transformers for Multilingual AES
• 4.5.2 Using MBERT to Score Essays Written in Persian
• 4.5.3 Assessment Technology, Equity, and Opportunity
• Note
• Appendix A
• Appendix B
• .......
• ....
• Instruction
• Topics
• 5 SmartWriting-Mandarin: An Automated Essay Scoring System for Chinese Foreign Language Learners
• 5.1 Introduction
• 5.2 Related Works
• 5.2.1 DNN-Based AES Systems
• 5.2.2 Chinese Automatic Essay Scoring and ACES
• 5.3 Details of SW-M
• 5.3.1 Preprocessing Module
• 5.3.2 Textual Features
• 5.3.3 Typos
• 5.3.4 Grammatical Errors
• 5.3.5 Scoring Model: A Fuzzy-Based Approach
• 5.4 Performance of SWM
• 5.5 Future Studies
• References.
• 6 NLP Application in the Hebrew Language for Assessment and Learning
• 6.1 Introduction
• 6.2 Hebrew Orthography and Morphology
• 6.2.1 Hebrew Orthography
• 6.2.2 Hebrew Morphology
• 6.2.2.1 The Verb System
• 6.2.2.2 The Noun System
• 6.2.2.3 Prepositions, Conjunctions, and Determiners
• 6.2.3 Text Length and Density
• 6.2.3.1 Hebrew Versus English Lexicon
• 6.2.3.2 Text Length
• 6.3 Morphological Lexicon and Corpora
• 6.3.1 Morphological Lexicon
• 6.3.2 Hebrew Corpora
• 6.3.2.1 M1 Corpus and the Annotated Corpus
• 6.3.2.2 News Corpus
• 6.3.3 Language Models
• 6.4 Computational Infrastructure for NLP in Hebrew
• 6.4.1 Tokenizer
• 6.4.2 Morphological Analyzer
• 6.4.3 Morphological Disambiguator
• 6.4.4 Semantic Disambiguator
• 6.4.5 Feature Extraction
• 6.4.5.1 Statistical Or "Surface" Features
• 6.4.5.2 Lexical Features
• 6.4.5.3 Morphological Features
• 6.4.5.4 Syntactic Features
• 6.4.5.5 Semantic Features
• 6.4.6 Grouping Text Features Into Linguistic Factors
• 6.4.7 Text Analysis Pipeline
• 6.5 Automated Essay Scoring
• 6.5.1 Score Prediction Algorithms
• 6.5.2 Grouping Features Into Macro-Features and Factors
• 6.5.3 Validity of NiteRater
• 6.5.3.1 Face and Content Validity - Identifying and Scoring Aberrant Essays
• 6.5.3.2 Predictive Or Criterion-Related Validity - Scoring of Classroom Essays
• 6.5.3.3 Predictive Or Criterion-Related Validity - Scoring of Tests for Admission to Higher Education
• 6.5.3.4 "True" Validity - Agreement With True Scores
• 6.5.3.5 Content Validity - Generalizing the Prediction Equation Across Prompts
• 6.5.4 Validity of Combined Computer and Human Scores
• 6.5.5 Quality Assurance of Essay Scoring
• 6.6 Other Applications of the Hebrew-NLP System
• 6.6.1 Providing Feedback to Essay Writers
• 6.6.2 Readability Assessment
• 6.6.2.1 Application to Textbooks (CET).
• 6.6.2.2 Simplification of Hebrew Legal Texts
• 6.6.3 Online Service to the Research Community
• 6.7 Summary, Open Issues, and Future Directions
• Section 2 Expanding Automated Evaluation: Reading, Speech, Mathematics, and Writing Research
• 7 Automated Scoring for NAEP Short-Form Constructed Responses in Reading
• 7.1 Introduction
• 7.1.1 Short-Form Constructed Responses
• 7.1.2 The Current Study
• 7.2 Method
• 7.2.1 Prompt-Specific Competition
• 7.2.1.1 Participants
• 7.2.1.2 Instruments
• 7.2.1.3 Procedure
• 7.2.1.4 Results
• 7.2.2 Generic Competition
• 7.2.2.1 Participants and Instruments
• 7.2.2.2 Procedure
• 7.2.2.3 Results
• 7.3 Discussion
• 7.3.1 Limitations
• 8 Automated Scoring and Feedback for Spoken Language
• 8.1 Introduction
• 8.2 Automated Scoring of Spoken Vs. Written Language
• 8.3 From the Rubric to Speech Features
• 8.4 Automated Speech Scoring System Architecture
• 8.4.1 Automatic Speech Recognition
• 8.4.2 Computing Speech Features
• 8.4.3 Filtering Models
• 8.4.4 Scoring Models
• 8.5 Operational Considerations
• 8.6 Providing Feedback to Language Learners
• 8.7 Speech Scoring Without Curated Features
• 8.8 Open Research Issues
• 8.9 Conclusion
• 9 Automated Scoring of Math Constructed-Response Items
• 9.1 Introduction
• 9.2 Anatomy of a Math Item
• 9.3 Challenges of Math Automated Scoring
• 9.3.1 Representation of Mathematics
• 9.3.2 Equivalence of Expressions
• 9.3.3 Evaluation of Mathematics
• 9.3.4 Extracting Mathematics From Prose
• 9.3.5 Understanding Reasoning
• 9.4 Injecting Mathematical Reasoning Into NLP Scoring Models
• 9.4.1 Scoring of Math-Only Responses
• 9.4.2 Scoring of Responses Containing Prose
• 9.4.3 Brief Comment On the Validity of Automated Scoring of Math CR Items
• 9.5 Empirical Study.
• 9.5.1 Ablation Study Results
• 9.5.2 Large Language Models for Math CR Scoring
• 9.6 Conclusion
• 10 We Write Automated Scoring: Using ChatGPT for Scoring in Large-Scale Writing Research Projects
• 10.1 Introduction
• 10.1.1 We Write Intervention
• 10.1.2 Theoretical Framework
• 10.2 Developing a ChatGPT-Based Scoring Algorithm to Evaluate the Efficacy of the We Write Intervention
• 10.2.1 Design of Measures
• 10.2.2 Human Scoring Scheme for Essay Quality
• 10.2.3 ChatGPT Scoring Model Architecture/Details
• 10.2.3.1 Refinement of Scoring
• 10.3 Score Validation: Comparing Human and ChatGPT Scoring
• 10.4 Discussion and Future Research
• 10.4.1 Score Tendencies
• 10.4.2 Agreement Between Scores
• 10.4.3 Generosity of Scoring
• 10.4.4 Correlation Across Proficiency Levels
• 10.4.5 Efficiency
• 10.5 Limitations
• 10.6 Conclusion
• Section 3 Innovations in Automated Writing Evaluation
• 11 Exploring the Role of Automated Writing Evaluation as a Formative Assessment Tool Supporting Self-Regulated Learning in Writing
• 11.1 Introduction
• 11.1.1 The Present Chapter
• 11.2 Does AWE Help Students Learn Evaluation Criteria?
• 11.2.1 Learning Evaluation Criteria: Summary and Future Directions
• 11.3 Does AWE Help Students Practice Writing Skills and Processes?
• 11.3.1 Practice Writing Skills and Processes: Summary and Future Directions
• 11.4 Does AWE Provide Understandable and Actionable Feedback?
• 11.4.1 Understandable and Actionable Feedback: Summary and Future Directions
• 11.5 Does AWE-Supported Peer Review Offer Benefits for Reviewers and Writers?
• 11.5.1 AWE-Supported Peer Review: Summary and Future Directions
• 11.6 Does AWE Support Students Taking Ownership of Their Learning?
• 11.6.1 Ownership of Learning: Summary and Future Directions
• 11.7 Conclusion.

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ISBN

• 9781040033241
• 1040033245
• 9781003397618
• 1003397611
• 9781040033340
• 1040033342

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