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ADL-MOOC: Adaptive Learning Through Big Data Analytics and Data Mining Algorithms for MOOCs

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 658))

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Abstract

Massive Open Online Courses (MOOCs) have had an impact in current higher education as an online phenomenon gathering momentum over the past couple of years.

However, one of the major challenges for MOOCs is capitalizing their potential as a tremendous data source for adaptive learning, whose large datasets growing exponentially are size-wise up to what has been recently named as “Big Data”.

In this paper, we present a specific proof-of-concept oriented approach for enriching adaptive learning by applying Big Data Analytics and Data Mining algorithms for MOOCs in order to facilitate subject- and context-sensitive teaching and learning experiences, which results in an innovative technology-enhanced learning solution for intuitive and personalised interactions of students and teachers with educational contents, tools and data.

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Notes

  1. 1.

    http://www.solaresearch.org/.

  2. 2.

    BIG: http://www.big-project.eu/.

  3. 3.

    SCAPE: http://www.scape-project.eu/.

  4. 4.

    MASSIF: http://www.massif-project.eu/.

  5. 5.

    http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_final_1.pdf.

  6. 6.

    NASA’s Big Data Mission: http://www.csc.com/cscworld/publications/81769/81773-supercomputing_the_climate_nasa_s_big_data_mission.

  7. 7.

    Big Data in Education: http://hortonworks.com/blog/big-data-in-education-part-2-of-2/.

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Acknowledgements

We thank our colleagues from Nimbeo Estrategia e Innovacion who provided insight and expertise that greatly assisted our research, although they may not agree with all of the interpretations/conclusions of this paper.

We thank Yuliana Gallardo for assistance with Bayesian Belief Network model, and for comments that greatly improved the manuscript.

Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad Carlos III de Madrid, Getafe, Spain

    Juan Miguel Gómez-Berbís & Ángel Lagares-Lemos

Authors
  1. Juan Miguel Gómez-Berbís
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  2. Ángel Lagares-Lemos
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Corresponding author

Correspondence to Ángel Lagares-Lemos .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Katty Lagos-Ortiz

  3. Universidad de Castilla-La Mancha, Toledo, Spain

    Gema Alcaraz-Mármol

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier del Cioppo

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Nestor Vera-Lucio

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Gómez-Berbís, J.M., Lagares-Lemos, Á. (2016). ADL-MOOC: Adaptive Learning Through Big Data Analytics and Data Mining Algorithms for MOOCs. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., del Cioppo, J., Vera-Lucio, N. (eds) Technologies and Innovation. CITI 2016. Communications in Computer and Information Science, vol 658. Springer, Cham. https://doi.org/10.1007/978-3-319-48024-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-48024-4_21

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