Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems : Emerging Research and Opportunities
Language: English
Published by Igi Global Scientific Publishing, 2018
- Softcover
- New
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- Title
- Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems : Emerging Research and Opportunities
- Author
- Tadlaoui, Mouenis; Khaldi, Mohamed; Carvalho, Rommel
- Publisher
- Igi Global Scientific Publishing
- Publication year
- 2018
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1522587020
- ISBN 13
- 9781522587026
Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities is a collection of research on the use of Bayesian networks and methods as a probabilistic formalism for the management of the learner model in adaptive hypermedia. It specifically discusses comparative studies, transformation rules, and case diagrams that support all phases of the learner model and the use of Bayesian networks and multi-entity Bayesian networks to manage dynamic aspects of this model. While highlighting topics such as developing the learner model, learning management systems, and modeling techniques, this book is ideally designed for instructional designers, course administrators, educators, researchers, and professionals.
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About the Author
Mohamed Khaldi , teacher-researcher in pedagogical engineering, e-Learning and Information Technology and Educational Communication, is a Professor of Higher Education at the Ecole Normale Supérieure Tetouan of Abdelmalek Essaadi University Morocco, where he is since 1987. He is a member since 2008 of the Laboratory of Computer Science, Operational Research and Applied Statistics (LIROSA), he is a member since 2017 of the team Computer Science and University Pedagogical Engineering (S2IPU). He is co-author of two books with the publisher IGI Global. He is the lead editor for the research topics Artificial Intelligence and Collaborative Learning in the Classroom for the journal Frontiers in the area of Artificial Intelligence for Human Learning and Behavior Change. He is a reviewer in several journals and scientific member in several international conferences and symposia.(ab27a1c7-b535-4cba-a39d-1565b32d1f2b)
Rommel N. Carvalho is the Auction & Delivery Science Lead at Facebook Marketing Science LATAM. From 2015 to 2017 he was the Chief Data Scientist of the Observatory of Public Spending at the Brazilian Office of the Comptroller General (CGU), where he lead a team of about 20 Data Scientists responsible for monitoring public expenses, finding fraud, and fighting corruption. He has received the second place in the 5th Chico Ribeiro Prize about Quality and Cost Information of Spending in the Public Sector. He finished his Postdoctoral at George Mason University (GMU) in the area of artificial intelligence, data mining, uncertainty, and knowledge discovery in May 2012. During the 3 years of his PhD, he was a Graduate Research Assistant in the Department of Systems Engineering and Operations Research at GMU, Virginia, USA. He received his Master in Computer Science and his Bachelor of Computer Science from University of Brasília (UnB), DF, Brazil, in 2008 and 2003, respectively. He has been working for CGU as an IT expert since 2005 and at UnB as a Professor on the Applied Computer Science Masters program since 2012, when it was created. From 2011 to 2012 he participated in the Transparency Portal team, where his key role was to be the main expert in Open Government Data (OGD). In the end of 2012 he started working as the leader of the Data Science team at the Department of Research and Strategic Information (DIE). One of the projects developed at DIE, the Reference Price Database, won the first place on the CONIP 2013 Excellence Award in the category Management and Geographical Information Systems. He has done research on fraud detection and prevention for the Brazilian Government and situation awareness for the U.S. Navy. With 12 years of experience in the area, he has produced more than 130 different technical outputs, including papers, book chapters, technical presentations, processes, among others.
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