The Routledge International Handbook of Automated Essay Evaluation

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.

Mark D. Shermis was the principal investigator and academic advisor for the Automated Student Assessment Prize. He is currently Principal for Performance Assessment Analytics, LLC. Dr. Shermis has also held faculty and administrative positions at the University of Houston-Clear Lake, University of Akron, University of Florida, Florida International, Indiana University-Purdue University Indianapolis (IUPUI), and the University of Texas, USA. He is a frequently cited expert on machine scoring and co-author (with Frank DiVesta) of Classroom Assessment in Action. Joshua Wilson is Associate Professor in the School of Education at the University of Delaware, USA. He researches ways that automation and artificial intelligence can improve assessment, teaching, and learning with a specific focus on automated feedback, automated scoring, and automated writing evaluation. Notably, his research has attracted the support of sponsors such as the Institute of Education Sciences of the U.S. Department of Education, the Spencer Foundation, and the Bill and Melinda Gates Foundation.

Foreword Jill Burstein Section 1: Introduction to AEE and Modern AEE Systems 1. Introduction to Automated Evaluation Mark D. Shermis and Joshua Wilson 2. Automated Essay Evaluation at Scale: Hybrid Automated Scoring/Hand Scoring in the Summative Assessment Program Corey Palermo and Arianto Wibowo 3. Exploration of the Stacking Ensemble Learning Algorithm for Automated Scoring of Constructed-Response Items in Reading Assessment Hong Jiao, Shuangshuang Xu, and Manqian Liao 4. Scoring Essays Written in Persian Using a Transformer-Based Model: Implications for Multilingual AES Tahereh Firoozi and Mark J. Gierl 5. SmartWriting-Mandarin: An Automated Essay Scoring System for Chinese Foreign Language Learners Tao-Hsing Chang and Yao-Ting Sung 6. NLP Application in the Hebrew Language for Assessment and Learning Yoav Cohen, Anat Ben-Simon, Anat Bar-Siman-Tov, Yona Doleve, Tzur Karelitiz, and Effi Levi Section 2: Expanding Automated Evaluation: Reading, Speech, Mathematics, and Writing Research 7. Automated Scoring for NAEP Short-Form Constructed Responses in Reading Mark D. Shermis 8. Automated Scoring and Feedback for Spoken Language Klaus Zechner and Ching-Ni Hsieh 9. Automated Scoring of Math Constructed-Response Items Scott Hellman, Alejandro Andrade, Kyle Habermehl, Alicia Bouy, and Lee Becker 10. We Write Automated Scoring: Using ChatGPT for Scoring in Large-Scale Writing Research Projects Kausalai (Kay) Wijekumar, Debra McKeown, Shuai Zhang, Pui-Wa Lei, Nikolaus Hruska, and Pablo Pirnay-Dummer 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 Joshua Wilson and Charles MacArthur 12. Supporting Students' Text-Based Evidence Use via Formative Automated Writing and Revision Assessment Rip Correnti, Elaine Lin Wang, Lindsay Claire Matsumura, Diane Litman, Zhexiong Liu, and Tianwen Li 13. The Use of AWE in Non-English Majors: Student Responses to Automated Feedback and the Impact of Feedback Accuracy Aysel Saricaoglu and Zeynep Bilki 14. Relationships Between Middle-School Teachers' Perceptions and Application of Automated Writing Evaluation and Student Performance Amanda Delgado, Joshua Wilson, Corey Palermo, Tania M. Cruz Cordero, Matthew C. Myers, Halley Eacker, Andrew Potter, Jessica Coles, and Saimou Zhang 15. Automated Writing Trait Analysis Paul Deane 16. Advances in Automating Feedback for Argumentative Writing: Feedback Prize as a Case Study Perpetual Baffour and Scott Crossley 17. Automated Feedback in Formative Assessment Harry A. Layman Section 4: Factors Affecting the Performance of Automated Evaluation 18. Using Automated Scoring to Support Rating Quality Analyses for Human Raters Stefanie A. Wind 19. Calibrating and Evaluating Automated Scoring Engines and Human Raters over Time Using Measurement Models Stefanie A. Wind and Yangmeng Xu 20. AI Scoring and Writing Fairness Mark D. Shermis 21. Automating Bias in Writing Evaluation: Sources, Barriers, and Recommendations Maria Goldshtein, Amin G. Alhashim, and Rod D. Roscoe 22. Explainable AI and AWE: Balancing Tensions between Transparency and Predictive Accuracy David Boulanger and Vivekanandan Suresh Kumar 23. Validity Argument Roadmap for Automated Scoring David Dorsey, Hillary Michaels, and Steve Ferrara Section 5: Technological Innovations: "Where Do We Go From Here?" 24. Redesigning Automated Scoring Engines to Include Deep Learning Models Sue Lottridge, Chris Ormerod, and Milan Patel 25. Automated Short-Response Scoring for Automated Item Generation in Science Assessments Jinnie Shin and Mark J. Gierl 26. Latent Dirichlet Allocation of Constructed Responses Jordan M. Wheeler, Shiyu Wang, and Allan S. Cohen 27. Computational Language as a Window into Cognitive Functioning Peter W. Foltz and Chelsea Chandler 28. Expanding AWE to Incorporate Reading and Writing Evaluation Laura K. Allen, Püren Öncel, and Lauren E. Flynn 29. The Two U's in the Future of Automated Essay Evaluation: Universal Access and User-Centered Design Danielle S. McNamara and Andrew Potter