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,
Terri
Terri
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
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