Automatic Extraction of Pedagogic Metadata from Learning Content

Article type: Research Article

Authors: Roy, Devshri | Sarkar, Sudeshna | Ghose, Sujoy

Affiliations: Computer Science & Engineering Department, Indian Institute of Technology, Kharagpur, India. E-mails: [email protected], [email protected], [email protected]

Abstract: 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.

Keywords: Learning object metadata, automatic metadata extraction, open repository

Journal: International Journal of Artificial Intelligence in Education, vol. 18, no. 2, pp. 97-118, 2008