Think big: learning contexts, algorithms and data science

Open access


Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection on their type to proceed to an extension of their characterisation. One of the hubs to make feasible the use of Big Data in operational contexts is to give a theoretical basis to which to refer. The Data, Information, Knowledge and Wisdom (DIKW) model correlates these four aspects, concluding in Data Science, which in many ways could revolutionise the established pattern of scientific investigation. The Learning Analytics applications on online learning platforms can be tools for evaluating the quality of teaching. And that is where some problems arise. It becomes necessary to handle with care the available data. Finally, a criterion for deciding whether it makes sense to think of an analysis based on Big Data can be to think about the interpretability and relevance in relation to both institutional and personal processes.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Ackoff R. L. (1989). From Data to Wisdom Journal of Applies Systems Analysis Vol. 16 3-9.

  • Alahuhta P. (2014) Big Data Analytics -Business Opportunities and Challenges. Digitalization-Key to Growth- Seminar in Espoo Finland 24.9.2014 Retrieved from

  • Anderson C. (2008). The end of theory. Will the Data Deluge Makes the Scientific Method Obsolete? Wired Magazine 16.07 Retrieved from

  • Box G. E. P. (1976) Science and Statistics Journal of the American Statistical Association Vol.71 pp. 791-799

  • Ayres I. (2008) Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart New York: Random House Publishing Group.

  • Blair D. C. (2002). Knowledge management: hype hope or help?. Journal of the American Society for Information Science and Technology 53(12) 1019-1028

  • Cameron W. B. (1963). Informal sociology: A casual introduction to sociological thinking. New York: Random House.

  • D. Cielen D. Meysman A. D. B.Ali M. (2016). Introducing Data Science-Big data machine learning and more using Python tools New York: Manning Shelter Island

  • Conway D. (2010). The data science venn diagram. Dataists Retrieved from

  • Cordoba R (2016). Foreword. In Daniel Big data and learning analytics in higher education: Current theory and practice.(pp. vii-viii). Switzerland: Springer

  • Daniel B. K. (Ed.) (2016). Big data and learning analytics in higher education: Current theory and practice. Switzerland: Springer

  • Data Science Association (2013). Terminology. Retrieved from

  • Silver N. (2012). The Signal and The Noise: Why Most Predictions Fail but Some Don’t. New York NY: The Penguin Press

  • Davenport T. H. & Prusak L. (1998). Working knowledge: How organizations manage what they know. Boston: Harvard Business Press.

  • De Francisci S. (2015). La visualizzazione dei Big Data. Documenti ISTAT. Retrieved from

  • De Mauro A. & Greco M. & Grimaldi M. (2015). What is big data? A consensual definition and a review of key research topics AIP Conference Proceedings 1644 97-104.

  • DeLillo D. (2003). Cosmopolis: A novel. New York: Scribner.

  • Elliott M. (2013). Big learning data. Alexandria VA: ASTD Press.

  • Frické M. (2009). The knowledge pyramid: a critique of the DIKW hierarchy. Journal of information science 35(2) pp. 131-142.

  • Gantz J. & Reinsel D. (2011). Extracting Value from Chaos. Retrieved from

  • Gartner. (2012). Big Data. Retrieved from

  • The Industry of the Future (2015). Ministère de l’Économie et des Finances Français. Retrieved from

  • Information Resources Management Association. (2016). Big data: Concepts methodologies tools and applications. Hershey PA: Information Science Reference.

  • Interaction Design Foundation (2016). Three Common Problems in Enterprise System User Experience Retrieved from

  • Jagadish H. V. Gehrke J. Labrinidis A. Papakonstantinou Y. Patel J. M. Ramakrishnan R. & Shahabi C. (2014). Big data and its technical challenges. Communications of the ACM 57(7) 86-94.

  • Jordan M. (2015). Modelos DIKW conceptuales valiosos Retrieved from

  • Kabakchieva D. & Stefanova K. (2015). Big Data Approach and Dimensions for Educational Industry. Economic Alternatives (4) pp. 47-59.

  • Klein J. (2014). Relational Data Lake SQLBlog Retrieved from

  • Kristensen A. (2014). Big Data Platform. Retrieved from

  • Leboeuf K. (2016). What happens in one internet minute?. Excelacom. Retrieved from

  • Lemberger P. Batty M. Morel M. Raffaëlli J. (2015) Big Data et machine learning: Manuel du data scientist Paris: Dunod

  • Marr B. (2015). Big Data: Using SMART big data analytics and metrics to make better decisions and improve performance. Chichester (UK):John Wiley & Sons.

  • Manyika J. et al. (2011). Big data: The next frontier for innovation competition and productivity. Mckinsey Digital. Retrieved from

  • Mayer-Schönberger V. & Cukier K. (2013). Big data: A revolution that will transform how we live work and think. Boston: Houghton Mifflin Harcourt.

  • Mayer-Schönberger V. & Cukier K. (2014). Learning with big data: The future of education. Boston: Houghton Mifflin Harcourt.

  • Minor K. (2013). How Big Data and Cognitive Computing are Transforming Insurance. Retrieved from

  • MIUR (2016). Rapporto del gruppo di lavoro Miur sui big data del 28.7.2016. Retrieved from

  • Omid M. (2014) How to characterize DIKW (Data Information Knowledge Wisdom) hierarchy?. Retrieved from

  • Petro B. (2011) Welcome to the Zettabyte Era Info Exponential. Retrieved from

  • Rao V. M. Kumari V. V. & Silpa N. (2015). An extensive study on leading research paths on big data techniques & technologies. Technology 6(12) 20-34.

  • Rowley J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science 33(2) pp. 163-180.

  • Silver N. (2012). The signal and the noise: Why so many predictions fail-but some don't. New York: Penguin Press.

  • Soloviev K. (2016). 3 Steps to a Data-Driven Content Quality Approach. Contentquo. Retrieved from

  • UNECE - United Nations Economic Commission for Europe (2013) Classification of Types of Big Data. Retrieved from

  • UNECE -United Nations Economic Commission for Europe (2014) How big is Big Data? Exploring the role of Big Data in Official Statistics. Retrieved from

  • Van Rijmenam M. (2014) Think Bigger: Developing a Successful Big Data Strategy for Your Business New York: AMACOM Div American Mgmt Assn.

  • Ward J.S. Barker A. (2013). Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821. Retrieved from

  • Wu M (2012) The Big Data Fallacy And Why We Need To Collect Even Bigger Data Techrunch Retrieved from≠-information-≠-insights/

  • Zikopoulos P.C. et al (2013) Harness the Power of Big Data. The IBM Big Data Platform. New York: Mc Graw Hill

Journal information
Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 653 457 15
PDF Downloads 269 157 6