Engineering Data-Driven Adaptive Trust-based e-Assessment Systems
Description
This book shares original innovations, research, and lessons learned regarding teaching and technological perspectives on trust-based learning systems. Both perspectives are crucial to enhancing the e-Assessment process.
In the course of the book, diverse areas of the computer sciences (machine learning, biometric recognition, cloud computing, and learning analytics, amongst others) are addressed. In addition, current trends, privacy, ethical issues, technological solutions, and adaptive educational models are described to provide readers with a global view on the state of the art, the latest challenges, and potential solutions in e-Assessment. As such, the book offers a valuable reference guide for industry, educational institutions, researchers, developers, and practitioners seeking to promote e-Assessment processes.
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Content
Forensic Analysis Recognition.- Plagiarism Detection.- Biometric Tools for Learner Identity in e-Assessment.- Engineering Cloud-based Technological Infrastructure.- Security and Privacy in the TeSLA Architecture.- Design and Implementation of Dashboards to Support Teachers Decision-Making Process in e-Assessment Systems.- Design and execution of TeSLA Pilots.- Ethical, Legal and Privacy Considerations for Adaptive Systems.- Underpinning Quality Assurance in Trust-based e-Assessment Procedures.
