Think big: learning contexts, algorithms and data science

Open access

Abstract

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.

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 http://www.slideshare.net/petterialahuhta/alahuhta-bigdataandanalytics24sep2014

Anderson, C., (2008). The end of theory. Will the Data Deluge Makes the Scientific Method Obsolete?, Wired Magazine, 16.07, Retrieved from https://www.wired.com/2008/06/pb-theory/

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 http://www.dataists.com/2010/09/thedata-science-venn-diagram/.

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 http://www.datascienceassn.org/code-of-conduct.html

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 http://www.istat.it/it/files/2015/05/Big-Data-Visualization-ForumPA2015-finale1.pdf

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. http://dx.doi.org/10.1063/1.4907823

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 http://www.emc.com/collateral/analystreports/idc-extracting-value-from-chaos-ar.pdf

Gartner. (2012). Big Data. Retrieved from http://www.gartner.com/it-glossary/big-data/

The Industry of the Future (2015). Ministère de l’Économie et des Finances Français. Retrieved from http://www.economie.gouv.fr/files/files/PDF/pk_industry-of-future.pdf

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 https://www.interaction-design.org/literature/article/three-common-problems-in-enterprise-system-user-experience

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 http://informationxdummies.blogspot.it/2015/05/modelos-dikw-conceptuales-valiosos.html

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 http://sqlblog.com/blogs/jorg_klein/archive/2014/12/18/relational-data-lake.aspx

Kristensen A. (2014). Big Data Platform. Retrieved from http://www.slideshare.net/ibmsverige/ibm-big-dataplatform

Leboeuf K. (2016). What happens in one internet minute?. Excelacom. Retrieved from http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute

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 http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-nextfrontier-for-innovation.

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 http://www.ibmbigdatahub.com/blog/how-big-data-and-cognitive-computing-are-transforming-insurance-part-2

MIUR (2016). Rapporto del gruppo di lavoro Miur sui big data del 28.7.2016. Retrieved from http://www.istruzione.it/allegati/2016/bigdata.pdf.

Omid, M. (2014) How to characterize DIKW (Data, Information, Knowledge, Wisdom) hierarchy?. Retrieved from http://www.researchgate.net/post/How_to_characterize_DIKW_Data_Information_Knowledge_Wisdom_hierarchy

Petro B. (2011) Welcome to the Zettabyte Era, Info Exponential. Retrieved from http://infox.billpetro.com/2011/06/05/welcome-to-the-zettabyte-era/

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 http://www.contentquo.com/blog/3-steps-to-data-driven-quality-approach/

UNECE - United Nations Economic Commission for Europe (2013), Classification of Types of Big Data. Retrieved from http://www1.unece.org/stat/platform/display/bigdata/Classification+of+Types+of+Big+Data

UNECE -United Nations Economic Commission for Europe (2014), How big is Big Data? Exploring the role of Big Data in Official Statistics. Retrieved from http://www1.unece.org/stat/platform/pages/viewpage.action?pageId=99484307

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 https://arxiv.org/abs/1309.5821v1

Wu, M (2012), The Big Data Fallacy And Why We Need To Collect Even Bigger Data, Techrunch, Retrieved from https://techcrunch.com/2012/11/25/the-big-data-fallacy-data-≠-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

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 283 278 13
PDF Downloads 152 148 12