Data in Education


What’s your take on big data and decision-making?
Photo credit: Foter.com
 Mine ranges from a very humane appeal, courtesy of Etlinger (2015) for the need to analyze data critically, to a call for appropriateness and validity, thanks to Gill, Borden & Halgren (2014) to the traps in decision-making, issued by Hammond, Keeney and Raiffa (1998); and still pass by an appeal for DDDM (Data-Driven Decision Making) to form teaching professionals for an informed instruction supplied by Mandinach (2012).
The point made about data being useless if not carefully analyzed in a given setting with explicit understanding and preparedness seems uncontroversial. After all, numbers can be anything to anyone depending of the context in which they occur as numbers do not, and will not, mean something if deprived from the obvious but often times easily forgotten idea that they are translated and related to students that we have the power and the responsibility to form and inform as educators.

Because the human being is unique as unique are our brains (Principle 1 in the Mind, Brain and Education science, according to OECD 2002, 2007(a), 2007(b); Tokuhama-Espinosa, 2008, 2010, 2014), then one cannot help but be aware and wary of any kind of collective quest to find a denominator and a remedy for instruction that caters to all.

In Education nowadays, there is not a one-size-fits-all approach hence the force and penetration among teachers that differentiation holds since its inception with the foundational work by Tomlinson (1999). The very position that Finland holds today not only in successful standards for achievement but also in instruction based on differentiation bears data rich proof (Hancock & Conway, 2011; Shalberg, 2007; Tung, 2012) to the fact that it is not by amassing data that education professionals will address the need for a better outcome, but rather by looking at and addressing the diverse needs in the classroom, schools, districts, states and nations. And those needs fawn out to involve students and teachers for they are the change agents that spur spiraling upwards or downwards cycles in the learning process. Investing in them is proving to be the surer path for an improved result in student achievement rates (Fullan, Rincon-Gallardo, & Hargreaves, 2015).

The same need that Mandinach (2012) desperately sees as missing in teacher formation, i.e., data literacy, could be better catered for if educational psychologists were left with the burden of dealing with data, thus leaving teacher’s pre- and in-service formation for the development of the human aspect that delves into the intricate and complex understanding of human structural functioning and developmental stages.

 Dealing pro-actively with data research that truly informs to form (or reform) is the issue at stake then. For that to occur, education stakeholders have to take steps to make sure that data gathered are relevant, relatable and informative to make sure that all involved parties know where they are leaving from and where they should be heading to.

And once again: what's your take on Data and Education?


References



Etlinger, S. (2014, September 1). “What do we do with all this big data?” [TED Talk] Retrieved on 01/13/2015 from   http://www.ted.com/talks/susan_etlinger_what_do_we_do_with_all_this_big_data?language=en

Fullan, M., Rincon-Gallardo, S., & Hargreaves, A. (2015). Professional capital as accountability. Education Policy Analysis Archives, 23(15). http://dx.doi.org/10.14507/epaa.v23.1998.

Gill, B., Borden, B. C., & Hallgren, K. (2014). A conceptual framework for data-driven decision making. Princeton, NJ: Mathematica Policy Research.

Hancock, L. and Conway, S. (2011). “Why are Finland’s Schools Successful?” Smithsonian Magazine [online]. Retrieved on 06/01/2017 from http://www.smithsonianmag.com/innovation/why-are-finlands-schools-successful-49859555/

Hammond, J. S., Keeney, R. L., Raiffa, H. (1998). The hidden traps in decision making. Harvard Business Review, September-October 1998: 47-58.

Mandinach, E. (2012). A perfect time for data use: Using data driven decision-making to inform practice. Educational Psychologist, 47(2), 71-85.

Tung, S. (2012). “How the Finnish school system outshines U.S. education”. Stanford News [online]. Retrieved on 06/01/2017 from http://news.stanford.edu/news/2012/january/finnish-schools-reform-012012.html

OECD. (2002). Understanding the brain: Towards a New Learning Science. Paris: OECD

OECD. (2007a). The Brain and Learning. Paris: OECD

OECD. (2007b). Understanding the brain: The birth of a learning science. Paris: OECD

Rohrer, D., & Pashler, H. (2012). Learning Styles: Where's the Evidence? Online Submission, 46(7), 634-635.

Sahlberg, P. (2007). Education policies for raising student learning: The Finnish approach. Journal of Education Policy, 22(2), 147-171.

Tokuhama-Espinosa, T. (2008). The new science of teaching and learning: Using the best of mind, brain, and education science in the classroom. Teachers College Press.

Tokuhama-Espinosa, T. (2010). Mind, brain, and education science: A comprehensive guide to the new brain-based teaching. WW Norton & Company.

Tokuhama-Espinosa, T. (2014). Making classrooms better: 50 practical applications of mind, brain, and education science. WW Norton & Company.

Tomlinson, C. (1999). The differentiated classroom: Responding to the needs of all learners. Alexandria, VA: Association for Supervision and Curriculum Development.

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