Responsible analytics and data mining in education : global perspectives on quality, support, and decision-making
- edited by Badrul H. Khan, Joseph Rene Corbeil, Maria Elena Corbeil.
- New York ; London : Routledge, Taylor & Francis Group, 2019.
- Copyright notice
- Physical description
- xxvi, 291 pages : illustrations ; 23 cm
At the library
Education Library (Cubberley)
|LB1028.43 .R48 2019||Unknown|
- Includes bibliographical references and index.
- 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.
- (source: Nielsen Book Data)
Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.
(source: Nielsen Book Data)
- Publication date
- Copyright date
- 9781138305885 (hbk)
- 113830588X (hbk)
- 9781138305908 (pbk)
- 1138305901 (pbk)
- 9780203728703 (ebk)
Browse related items
Start at call number: