Leveraging Cognitive Computing for Multi-class Classification of E-learning Videos

  • Danilo Dessì
  • Gianni Fenu
  • Mirko Marras
  • Diego Reforgiato Recupero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

Abstract

Multi-class classification aims at assigning each sample to one category chosen among a set of different options. In this paper, we present our work for the development of a novel system for multi-class classification of e-learning videos based on the covered educational subjects. The audio transcripts and the text depicted into visual frames are extracted and analyzed by Cognitive Computing tools, going over the traditional term-based similarity approaches. Preliminary experiments demonstrate effectiveness and capabilities of the system, suggesting that semantic analysis improves the performance of multi-class classification.

Keywords

Cognitive computing Multi-class classification E-learning video classification Semantic classification 

Notes

Acknowledgments

Danilo Dessì and Mirko Marras gratefully acknowledge Sardinia Regional Government for the financial support of their PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020 - Axis III Education and Training, Thematic Goal 10, Priority of Investment 10ii, Specific Goal 10.5).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Danilo Dessì
    • 1
  • Gianni Fenu
    • 1
  • Mirko Marras
    • 1
  • Diego Reforgiato Recupero
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

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