Semantic-enhanced Recommendation of Video Lectures

Autor(es):Mostrar menos -

Macedo Borges, Marcos Vinicius ; dos Reis, Julio Cesar ; Chang, M ; Sampson, DG ; Huang, R ; Gomes, AS ; Chen, NS ; Bittencourt, II ; Kinshuk ; Dermeval, D ; Bittencourt, IM

• Tipo de documento: Artigo Científico
• Fonte: 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2019); v. N/A, p. 5-pg., 2019-01-01.

Resumo

Learning support systems explore several audiovisual resources to consider individual needs and learning styles aiming to stimulate learning experiences. However, the large amount of educational content in different formats and the possibility of making them available in a fragmented way turns difficult the tasks of accessing these resources and understanding the concepts under study. Although literature has proposed approaches to explore explicit semantic representation through artifacts such as ontologies in learning support systems, this research line still requires further investigation efforts. In this paper, we propose a method for recommending educational content by exploring the use of semantic annotations over textual transcriptions from video lessons. Our investigation addresses the difficulties in extracting entities from natural language texts as subtitles of videos. We report on major challenges to achieve the representation of video transcriptions as semantic annotations for automatic recommendation of educational content. (AU)

• Processo FAPESP: 17/02325-5 - EvOLoD: evolução de dados interconectados na Web Semântica
• Beneficiário: Julio Cesar dos Reis
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 18/00313-2 - Anotação semântica para recomendação de conteúdos educacionais
• Beneficiário: Marcos Vinicius Macêdo Borges
• Modalidade de apoio: Bolsas no Brasil - Mestrado