Full Description
The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large-scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.
Contents
Chapter 1. On Integrating Psychometrics and Learning Analytics in Complex Assessments; Robert J. Mislevy.
Chapter 2. Exploring Process Data in Problem-Solving Items in Computer-Based Large-Scale Assessments: Case Studies in PISA and PIAAC; Qiwei He, Matthias von Davier, and Zhuangzhuang Han.
Chapter 3. The Use of Data Mining Techniques to Detect Cheating; Sarah L. Thomas and Dennis D. Maynes.
Chapter 4. Selected Applications of Data Science in Cyber Security; Yue (Richard) Xie.
Chapter 5. Assessing Learner-Driven Constructs in Informal Learning Environments: Synergies Created by the Nexus of Psychometrics, Learning Analytics, and Educational Data Mining; Lori C. Bland.
Chapter 6. Measuring Rater Effectiveness: New Uses of Value-Added Modeling in Competency-Based Education; B. Brian Kuhlman.
Chapter 7. Ranking Documents in Online Enterprise Social Network; Alex H. Wang and Umeshwar Dayal.
Chapter 8. Methods for Measuring Learning Evaluation in the Context of E-Learning; Matthew Pietrowski, Roopa Sanwardeker, and David Witkowski.
Chapter 9. High Level Strategic Approaches for Conducting Big Data Studies in Assessment; Manfred M. Straehle, Liberty J. Munson, Austin Fossey, and Emily Kim.
Chapter 10. Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs; Evgenia Samoilova, Florian Keusch, and Frauke Kreuter.
