Responsible Analytics and Data Mining in Education

Dedication. Foreword, Ben Kei Daniel. Preface. Acknowledgements I. Introduction 1. A Framework for Implementing Responsible Data Mining and Analytics in Education, Maria Elena Corbeil, Joseph Rene Corbeil, and Badrul H. Khan 2. Evolution and Facets of Data Analytics for Educational Data Mining and Learning Analytics, Venkat Gudivada, Dhana L. Rao, and Junhua Ding 3. Historical and Theoretical Perspectives of Data Analytics and Data Mining in Education, Didem Tufan and Soner Yildirim 4. Mapping Responsible Learning Analytics: A Critical Proposal, Paul Prinsloo and Sharon Slade II. Pedagogical and Interface Design Issues 5. Data Sources for Educators: Mining Meaningful Data for Course and Program Decision Making, Rick Voithofer and Amir Golan 6. The Role of Data Analytics in Education: Possibilities & Limitations, Robert Moore 7. Learning about Learning Online: The Methodology of Discourse Analytics, Linda Harasim III. Technological and Resource Support Issues 8. Supporting Data Analytics in Education: Human and Technical Resources for Collecting, Storing, Analyzing, and Mining Data, Dhana L. Rao, Junhua Ding, and Venkat N. Gudivada 9. User Model Interoperability in Education: Sharing Learner Data Using the Experience API and Distributed Ledger Technology, Konstantinos Karoudis and George D. Magoulas 10. Human and Technological Resources Needed to Develop and Sustain a City-Wide Educational Data Observatory, Rachel Shanks, Bruce Scharlau, Hataichanok Saevanee, and Kevin Stelfox IV. Evaluation and Ethical Issues 11. Assessing Data Quality: Determining What Data to Trust and Use, Barbara Filipczyk, Krzysztof Kania, Grzegorz Filipczyk, and Joanna Paliszkiewicz 12. Ethical Issues and Potential Unintended Consequences of Data-Based Decision Making, Rick Voithofer and Marcia Ham 13. The Application of Ethical Themes for Responsible Data-Driven Decision Making in Education, Gwen White V. Management and Institutional Issues 14. Contingency Planning for Data Breaches, Christopher Stevens and Todd Lindsay Wirth 15. Minimizing Data Errors through Reflective Process and Knowledge Management Structures, Mark E. Deschaine, Raymond W. Francis, and Sue Ann Sharma

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Unsupervised Machine Learning;Learning Analytics Applications;Badrul H. Khan;Vice Versa;Joseph Rene Corbeil;Support Vector Machines;Maria Elena Valdes-Corbeil;Information Visualization;Responsible Analytics and Data Mining in Education;High Performance Computing;e-learning;Learning Analytics;EDM;E-Learning Framework;data analysis;information and communication technology;Student Engagement;learning technologies;Sensitive Information;progam evaluation;Distributed Ledger Technology;data-based decision-making;Adaptive Learning Systems;data-driven errors;Learning Analytics Research;cybersecurity;Data Mining;institutional management;Educational Data;interface design;Big Data;web-based instruction;Smart Contracts;Online Week;Maria Elena Corbeil;Fitbit Charge;Venkat N. Gudivada;Early Alert Systems;Dhana L. Rao;MOOC Context;Junhua Ding;Missing Data;Didem Tufan;Nonnegative Matrix Factorization;Soner Yildirim;Multiple Linear Regression;Paul Prinsloo;Sharon Slade;Rick Voithofer;Amir M. Golan;Robert L. Moore;Linda Harasim;Konstantinos Karoudis;George D. Magoulas;Rachel Shanks;Bruce Scharlau;Hataichanok Saevanee;Kevin Stelfox;Barbara Filipczyk;Krzysztof Kania;Grzegorz Filipczyk;Joanna Paliszkiewicz;Marcia Ham;Gwen White;Christopher R. Stevens;Todd L. Wirth;Mark E. Deschaine;Ray Francis;Sue Ann Sharma