Name
Abstract | ||
|---|---|---|
In this paper an approach for building an intelligent tutoring system is presented, based on a multi-agent architecture and combined with ontologies for knowledge representation. The system developed is focused on a bottom up, reactive generation of an active sequence of knowledge units regarding a set of adjustable, high level learning goals. The learning process begins with a set of simple learning goals that require a few learning objects and as the educational process proceeds, the student has to achieve higher learning outcomes that combine other low level outcomes which have been already achieved. The system is able to adapt to studentâs learning profile and progress by applying proper learning tactics to prioritize through a weight calculation scheme the sequence of the learning outcomes to achieve. The main components of the system consisting of ontological models of the learner and the subject under study, gateway agents and tutor agents with their core modules (learning space management and learning tactics control) are explained and a detailed description of their interaction is given in the context of an example application. Finally, the advantages of the proposed approach are laid out, especially in the setting of a distance learning education system. |
Year | Venue | Field |
|---|---|---|
2015 | CSEDU | Robot learning,Multi-task learning,Instance-based learning,Active learning (machine learning),Computer science,Knowledge management,Action learning,Synchronous learning,Adaptive learning,Proactive learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stamatis Panagiotis | 1 | 0 | 0.34 |
| Ioannis Panagiotopoulos | 2 | 12 | 2.79 |
| Christos Goumopoulos | 3 | 104 | 18.60 |
| Achilles Kameas | 4 | 355 | 50.94 |