Machine Learning Natural Language Processing And Psychometrics
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Machine Learning, Natural Language Processing, and Psychometrics
With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better understand the assessment products or accuracy and the process how an item product was attained. The analysis of the conventional and non-conventional assessment data calls for more methodology other than the latent trait modeling. Natural language processing (NLP) methods and machine learning algorithms have been successfully applied in automated scoring. It has been explored in providing diagnostic feedback to test-takers in writing assessment. Recently, machine learning algorithms have been explored for cheating detection and cognitive diagnosis. When the measurement field promote the use of assessment data to provide feedback to improve teaching and learning, it is the right time to explore new methodology and explore the value added from other data sources. This book presents the use cases of machine learning and NLP in improving the assessment theory and practices in high-stakes summative assessment, learning, and instruction. More specifically, experts from the field addressed the topics related to automated item generations, automated scoring, automated feedback in writing, explainability of automated scoring, equating, cheating and alarming response detection, adaptive testing, and applications in science assessment. This book demonstrates the utility of machine learning and NLP in assessment design and psychometric analysis.
Research for Practical Issues and Solutions in Computerized Multistage Testing
This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST). As a sequel to the widely acclaimed Computerized Multistage Testing: Theory and Applications (2014) by Yan, von Davier, and Lewis, this volume delves into the experiences, considerations, challenges, and lessons learned over the past years. It also offers practical approaches and solutions to the issues encountered. The topics covered include purposeful MST designs, practical approaches for optimal design, assembly strategies for accuracy and efficiency, hybrid designs, MST with natural language processing, practical routing considerations and methodologies, item calibration and proficiency estimation methods, routing and classification accuracy, added value of process data, prediction and evaluation of MST performance, cognitive diagnostic MST, differential item functioning, robustness of statistical methods, simulations, test security, the new digital large-scale Scholastic Aptitude Test, software for practical assessment and simulations, artificial intelligence impact, and the future of adaptive MST. This volume is intended for students, faculty, researchers, practitioners, and education officers in the fields of educational measurement and evaluation in the United States and internationally.
The Routledge International Handbook of Automated Essay Evaluation
The Routledge International Handbook of Automated Essay Evaluation (AEE) 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 endeavors. It presents a comprehensive overview of AEE's current applications, including its extension into reading, speech, mathematics, and writing research; modern automated feedback systems; critical issues in automated evaluation such as psychometrics, fairness, bias, transparency, and validity; and the technological innovations that fuel current and future developments in this field. As AEE approaches a tipping point of global implementation, this Handbook stands as an essential resource, advocating for the conscientious adoption of AEE tools to enhance educational practices ethically. The Handbook will benefit readers by equipping them with the knowledge to thoughtfully integrate AEE, thereby enriching educational assessment, teaching, and learning worldwide. Aimed at researchers, educators, AEE developers, and policymakers, the Handbook is poised not only to chart the current landscape but also to stimulate scholarly discourse, define and inform best practices, and propel and guide future innovations.