Article

Automatic Extraction of Pedagogic Metadata from Learning Content.

I. J. Artificial Intelligence in Education 01/2008; 18:97-118.
Source: DBLP
0 0
 · 
0 Bookmarks
 · 
90 Views
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).
    HT'11, Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, Eindhoven, The Netherlands, June 6-9, 2011; 01/2011
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: The iLOG Project (intelligent learning object guide) is designed to augment multimedia learning objects with information about (1) how a learning object has been used, (2) how it has impacted instruction and learning, and (3) how it should be used. The goal of the project is to generate metadata tags from data collected while students interact with learning objects; these metadata tags can then be used to help teachers identify learning objects that match the educational and experiential backgrounds of their students. The project involves the development of an agent-based intelligent system for tracking student interaction with learning objects, in tandem with an extensive learning research agenda. This paper provides an overview of this NSF-funded project, focusing on the instructional approach and research on varying levels of active learning and feedback. Using a randomized design and a hierarchical linear modeling framework, research showed that the active learning conditions resulted in significantly higher student learning. The elaborative feedback results approached (p = .056), but did not reach, the established significance criteria of alpha = .05. Both active learning conditions and one of the elaborative feedback conditions resulted in significantly higher content assessment scores compared to a control group.
    Frontiers in Education Conference, 2009. FIE '09. 39th IEEE; 11/2009
  • [show abstract] [hide abstract]
    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.
    Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, April 11-15, 2011, Paris, France; 01/2011

Full-text (2 Sources)

View
11 Downloads
Available from
Jun 6, 2013