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B-Learning and Big Data: Use in Training an Engineering Course

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Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

Is presents a case study in a descriptive work of qualitative Court that seeks to evaluate the advantages of the deployment and the use of the b-learning methodology and big data in pedagogical processes. There is the need for evolution of the type of traditional education currently practised in the University by a methodology that allows for greater participation and responsibility on the part of the student and which present an opportunity for development of independent learning skills. Initially develops a theoretical reference framework associated with the traditional teaching, B-learning and Big data with its approach to the field of education. Subsequently, is an approach to the existing problems in a case study employing the use of descriptive records, participant observation and interviews not structured to analyze and compare the academic performance of students in a course implementing b-learning vs. a course with traditional methods.

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Author information

Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Leonardo Emiro Contreras Bravo, Jose Ignacio Rodriguez Molano & Giovanny Mauricio Tarazona Bermudez

Authors
  1. Leonardo Emiro Contreras Bravo
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  2. Jose Ignacio Rodriguez Molano
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  3. Giovanny Mauricio Tarazona Bermudez
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Corresponding author

Correspondence to Leonardo Emiro Contreras Bravo .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Contreras Bravo, L.E., Rodriguez Molano, J.I., Tarazona Bermudez, G.M. (2017). B-Learning and Big Data: Use in Training an Engineering Course. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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