Cartoon: Big Data / CC BY 2.0
Aloha all -
This week, we explored the concept of Big Data and Learning Analytics. I must say I had heard about the concept but hadn't delved into it much. After reading more about it, I now have a better idea why I get certain coupons or emails with suggested items for me to purchase. With the use of web-tracking tools, company seek to identify trends and behaviors that will help to insert their products or stores into our shopping habits to increase sales. I'm not sure I'm all that comfortable with it but I was able to gain a broad understanding on how many companies collect these massive amounts of data to individualize the customer experience as they compete for the almighty consumer dollar. When I read the article on how Target has used Big Data extensively to target pregnant women to increase purchases at their location, it was interesting to see how it played out. Ultimately, by interspersing the baby ad items amongst other random items such as lawnmowers and wine, they were able to market individually in a subtle way that resulted in a positive outcome (more sales). I also found the background on habit forming with cue, routine and reward interesting and see how sometimes piggybacking onto already formed habits can induce more customer sales as well.
Learning analytics describes applying the use of Big Data in the educational realm. In the 2014 Horizon Report, it was identified as having a time to adoption horizon of one year or less. Using the business model of using large amounts of data collected to analyze, predict and identify consumer behavior, educators are now looking into applying the same process to personalize the experience for learners and increase student engagement. One of the examples presented was how some colleges are using Big Data collected to potentially predict student challenges and provide an opportunity for the schools to offer academic support resources and the like to help students transition to college successfully. These analytics help colleges to identify ways to use resources in a way to reap the greatest benefits and contribute to student success. As budgets constraints increase, institutions are able to focus limited resources in areas that align with student needs. Although benefits can be gained, there are also the considerations in the areas of ethics, legality and student privacy. Organizations must be cautious on interpretation of Big Data as analytics can show correlations, but doesn't necessarily provide causal information and even the best systems can lead to misclassification or profiling. And, of course, there is the challenge of figuring out which interventions can be most effective to help the students. I think that the mining of Big Data can be effectively applied to education, but a lot will depend on how this empirical evidence is interpreted and applied. It will be interesting how it plays out in the near future as Big Data analysis leads to implementation of targeted strategies to benefit learners.
A hui hou,
Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium. pp. 38-39. Retrieved from http://www.nmc.org/pdf/2014-nmc-horizon-report-he-EN.pdf
Duhigg, C. (2012, February 18). How Companies Learn Your Secrets. The New York Times. Retrieved from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
EDUCAUSE Learning Initiative. (2010). 7 Things you should know about analytics. Retrieved from http://net.educause.edu/ir/library/pdf/ELI7059.pdf