Robot-Oriented Generative Learning Objects: An Agent-Based Vision

2016 ◽  
pp. 247-257 ◽  
Author(s):  
Vytautas Štuikys ◽  
Renata Burbaitė ◽  
Vida Drąsutė ◽  
Kristina Bespalova
Keyword(s):  
Learning Objects ◽  
Generative Learning ◽  
Agent Based

Toward an Agent-Based Platform for Learning Objects Management

2006 ◽  
pp. 111-119
Author(s):  
Nicandro Farias Mendoza ◽  
Gabriel Cruz ◽  
Orvil Ceja ◽  
Miguel Díaz ◽  
José Macías
Keyword(s):  
Learning Objects ◽  
Agent Based

Intelligent Learning Objects: An Agent Based Approach of Learning Objects

2004 ◽  
pp. 886-888 ◽  
Author(s):  
Ricardo Azambuja Silveira ◽  
Eduardo Rodrugues Gomes ◽  
Vinicius Heidrich Pinto ◽  
Rosa Maria Vicari
Keyword(s):  
Learning Objects ◽  
Agent Based

Multi-Agent Based Dynamic E-Learning Environment

2011 ◽  
pp. 142-158 ◽  
Author(s):  
Saleh AlZahrani ◽  
Aladdin Ayesh ◽  
Hussein Zedan
Keyword(s):  
Learning Environment ◽  
Learning Objects ◽  
Agent Based ◽  
Development Framework ◽  
The World ◽  
E Learning ◽  
Multi Agent ◽  
The Subject ◽  
And Training ◽  
Agent Development

Grids are increasingly being used in applications, one of which is e-learning. As most of business and academic institutions (universities) and training centres around the world have adopted this technology in order to create, deliver and manage their learning materials through the Web, the subject has become the focus of investigate. Still, collaboration between these institutions and centres is limited. Existing technologies such as grid, Web services and agents are promising better results. In this article the authors support building our architecture Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE) by combining those technologies via Java Agent DEvelopment Framework (JADE). By describing these agents in details, they prove that agents can be implemented to work well to extend the autonomy and interoperability for learning objects as data grid.

Learning Objects and Generative Learning for Higher Order Thinking

2011 ◽  
pp. 702-722
Author(s):  
Tan Wee Chuen ◽  
Baharuddin Aris ◽  
Mohd Salleh Abu
Keyword(s):  
Higher Order Thinking ◽  
Thinking Skills ◽  
Learning Objects ◽  
Higher Order ◽  
Learning System ◽  
Generative Learning ◽  
Web Based ◽  
Higher Order Thinking Skills ◽  
Web Based Learning ◽  
Integration Of Learning

This chapter aims to guide the readers through the design and development of a prototype Web-based learning system based on the integration of learning objects with the principles of generative learning to improve higher order thinking skills. The chapter describes the conceptual model called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) which was used to design and build a technology- supported learning environment. The chapter then describes how the effectiveness of the Web-based learning system was evaluated and reflects on the importance of the findings more generally.

scholarly journals Model-driven processes and tools to design robot-based generative learning objects for computer science education

2016 ◽  
Vol 129 ◽  
pp. 48-71 ◽  
Author(s):  
Vytautas Štuikys ◽  
Renata Burbaitė ◽  
Kristina Bespalova ◽  
Giedrius Ziberkas
Keyword(s):  
Science Education ◽  
Computer Science ◽  
Computer Science Education ◽  
Learning Objects ◽  
Generative Learning ◽  
Model Driven

On the Generation of Random Data for Auto-Generative Learning Objects

2019 ◽  
Author(s):  
Felicia-Mirabela Costea ◽  
Ciprian-Bogdan Chirila ◽  
Oana-Sorina Chirila ◽  
Vladimir-Ioan Cretu
Keyword(s):  
Learning Objects ◽  
Generative Learning ◽  
Random Data

scholarly journals BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

2013 ◽  
Vol 2 (1) ◽  
pp. 6 ◽  
Author(s):  
Paula Rodriguez ◽  
Valentina Tabares ◽  
Nestor Duque ◽  
Demetrio Ovalle ◽  
Rosa Vicari
Keyword(s):  
Learning Objects ◽  
Agent Based Model ◽  
Agent Based ◽  
Learner Profile
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