Name
Abstract | ||
|---|---|---|
R This paper propases two intelligent agents for learning object automated sequencing using heuristic and local search. In e-learning iniliatives, sequencing problem concerns arranging a particular se! 01 learning units in a suitable succession for a particular learner. Sequencing ls usualIy perfonned by instructors, who create general and ordered series ra/her than leamer personalized sequences. Eleaming standards are promoted in arder lo ensure interoperability. Campe/enejes are used lo define rela/fans between learning objects within a sequence, so tha! lhe sequencing problem tums into a pennutation problem andAl techniques can be used to solve it. Heuristic and Local Search are two 01 such techniques. An implementation 01 the A* algorithm and Ni!! Climbing algorithm, lor leaming object sequencing, are presented and their peifonnance in a real scenario is discussed. |
Year | Venue | Keywords |
|---|---|---|
2010 | CSREA EEE | local search |
Field | DocType | Citations |
Intelligent agent,Heuristic,Guided Local Search,Computer science,Interoperability,Learning object,Artificial intelligence,Bioinformatics,Local search (optimization),Tabu search,Best-first search | Conference | 1 |
PageRank | References | Authors |
0.47 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Garcia | 1 | 35 | 11.13 |
| Luis de Marcos | 2 | 112 | 16.95 |
| Eva García | 3 | 2 | 5.23 |
| José Antonio Gutiérrez | 4 | 43 | 7.27 |