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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    • "Web search engines provide a low cost index access that can find the required resources in the large portion of documents on the Internet. However, this indexing is not suitable for more sophisticated retrieval tasks [19] that require the consideration of pedagogical relationships between documents. "
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    ABSTRACT: The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of online content and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only a few works have been proposed to distinguish between prerequisite and outcome concepts, and to the best of our knowledge, no method has been introduced so far to benefit from this information to produce a meaningful learning path. To this aim, this article first describes a concept annotation method that relies on machine-learning techniques to predict the class of each concept-prerequisite or outcome-on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resource sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real dataset built from online resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the online content.
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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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    • "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: We present a framework for the automatic annotation of learning objects (LOs) with empirical usage metadata. Our implementation of the Intelligent Learning Object Guide (iLOG) was used to collect interaction data of over 200 students' interactions with eight LOs. We show that iLOG successfully tracks student interaction data that can be used to automate the creation of meaningful empirical usage metadata that is based on real-world usage and student outcomes.
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