Name
Abstract | ||
|---|---|---|
The objective of any tutoring system is to provide meaningful learning to the learner, thence it is important to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. In this paper, we study the problem of identifying defined concepts and prerequisite concepts from learning resources available on the web. Statistics and machine learning tools are exploited in order to predict the class of each concept. Two groups of features are constructed to categorize the concepts: contextual features and local features. The contextual features enclose linguistic information and the local features contain the concept properties such as font size and font weigh. An aggregation method is proposed as a solution to the problem of the multiple occurrences of a defined concept in a document. This paper shows that best results are obtained with the SVM classifier than with other classifiers. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CIDM.2011.5949296 | Computational Intelligence and Data Mining |
Keywords | Field | DocType |
computer aided instruction,learning (artificial intelligence),linguistics,SVM classifier,contextual features,learning resources,local features,machine learning tools,tutoring system | Algorithmic learning theory,Multi-task learning,Instance-based learning,Semi-supervised learning,Active learning (machine learning),Computer science,Concept learning,Unsupervised learning,Artificial intelligence,Natural language processing,Machine learning,Learning classifier system | Conference |
ISBN | Citations | PageRank |
978-1-4244-9926-7 | 1 | 0.36 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahar Changuel | 1 | 26 | 2.76 |
| Nicolas Labroche | 2 | 139 | 17.87 |