Automatic Extraction of Pedagogic Metadata from Learning Content.

I. J. Artificial Intelligence in Education 01/2008; 18:97-118.
Source: DBLP


Annotating learning material with metadata allows easy reusability by different learning/tutoring systems. Several metadata standards have been developed to represent learning objects and courses. A learning system needs to use pedagogic attributes including document type, topic, coverage of concepts, and for each concept the significance and the role. Moreover, in order to have a flexible and reusable repository of e-learning materials, it is necessary that the annotation of the documents with such metadata be done in an automatic fashion as far as possible. This paper describes the attributes that represent some important pedagogic characteristics of learning materials. To reduce the overhead of manual annotation we have explored the feasibility of automatic annotation of learning materials with metadata. This facilitates the creation of an elearning open repository for storing these annotated learning materials, which can be used by learning systems. The automatic annotation is based on a domain knowledge base and a number of algorithms like standard classification algorithms, parsing and analysis of documents have been used for this purpose. The results show a fair degree of accuracy, which may be improved in future using more sophisticated algorithms.

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Available from: Sudeshna Sarkar, Oct 05, 2015
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    • "Roy et al. [7] uses an automatic annotation tool for annotating learning objects with pedagogical metadata such as concepts/concept significance, type of concepts and learning resource type. Concept type (i.e. "
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    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).
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    • "Existing work on automatic generation of standardized LO metadata [4], [5] primarily focuses on extracting ontological and taxonomic information from raw learning content. This work will help a search engine in the retrieval of LOs that more closely match the target topic, thus satisfying need (1). "
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    ABSTRACT: The World Wide Web is composed of a large amount of learning content and an e-learner can retrieve learning materials from the Web. The different learners may have different learning requirements. Because the preferences and learner's requirement can vary greatly across individuals, a personalized retrieval system must be tailored so that it should be able to provide a learner with learning materials that he requires. The retrieval system should decide whether a document is relevant to the learner based on the curriculum requirement, the learner profile and the type of the learning material. We have implemented an information retrieval system for retrieving learning materials to satisfy the learners' need. To retrieve the personalized search results, the system looks into the learner profile, the domain knowledge and the automatically metadata annotated documents retrieves from the Web. To evaluate the performance of the system, many queries were processed out by our system.
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