big data


Center for Digital Education (CDE)

real-time impact on curriculum structure, instruction delivery and student learning, permitting change and improvement. It can also provide insight into important trends that affect present and future resource needs.

Big Data: Traditionally described as high-volume, high-velocity and high-variety information.
Learning or Data Analytics: The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Educational Data Mining: The techniques, tools and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings.
Predictive Analytics: Algorithms that help analysts predict behavior or events based on data.
Predictive Modeling: The process of creating, testing and validating a model to best predict the probability of an outcome.

Data analytics, or the measurement, collection, analysis and reporting of data, is driving decisionmaking in many institutions. However, because of the unique nature of each district’s or college’s data needs, many are building their own solutions.

For example, in 2014 the nonprofit company inBloom, Inc., backed by $100 million from the Gates Foundation and the Carnegie Foundation for the Advancement of Teaching, closed its doors amid controversy regarding its plan to store, clean and aggregate a range of student information for states and districts and then make the data available to district-approved third parties to develop tools and dashboards so the data could be used by classroom educators.22

Tips for Student Data Privacy

Know the Laws and Regulations
There are many regulations on the books intended to protect student privacy and safety: the Family Educational Rights and Privacy Act (FERPA), the Protection of Pupil Rights Amendment (PPRA), the Children’s Internet Protection Act (CIPA), the Children’s Online Privacy Protection Act (COPPA) and the Health Insurance Portability and Accountability Act (HIPAA)
— as well as state, district and community laws. Because technology changes so rapidly, it is unlikely laws and regulations will keep pace with new data protection needs. Establish a committee to ascertain your institution’s level of understanding of and compliance with these laws, along with additional safeguard measures.
Make a Checklist Your institution’s privacy policies should cover security, user safety, communications, social media, access, identification rules, and intrusion detection and prevention.
Include Experts
To nail down compliance and stave off liability issues, consider tapping those who protect privacy for a living, such as your school attorney, IT professionals and security assessment vendors. Let them review your campus or district technologies as well as devices brought to campus by students, staff and instructors. Finally, a review of your privacy and security policies, terms of use and contract language is a good idea.
Communicate, Communicate, Communicate
Students, staff, faculty and parents all need to know their rights and responsibilities regarding data privacy. Convey your technology plans, policies and requirements and then assess and re-communicate those throughout each year.

“Anything-as-a-Service” or “X-as-a-Service” solutions can help K-12 and higher education institutions cope with big data by offering storage, analytics capabilities and more. These include:
• Infrastructure-as-a-Service (IaaS): Providers offer cloud-based storage, similar to a campus storage area network (SAN)

• Platform-as-a-Service (PaaS): Opens up application platforms — as opposed to the applications themselves — so others can build their own applications
using underlying operating systems, data models and databases; pre-built application components and interfaces

• Software-as-a-Service (SaaS): The hosting of applications in the cloud

• Big-Data-as-a-Service (BDaaS): Mix all the above together, upscale the amount of data involved by an enormous amount and you’ve got BDaaS


Use accurate data correctly
Define goals and develop metrics
Eliminate silos, integrate data
Remember, intelligence is the goal
Maintain a robust, supportive enterprise infrastructure.
Prioritize student privacy
Develop bullet-proof data governance guidelines
Create a culture of collaboration and sharing, not compliance.

more on big data in this IMS blog:

Disruptive Tech Trends

5 Disruptive Tech Trends That Could Dominate in 2016

Andres Cardenal (IoT). The Internet of Things

Tim Brugger (Big Data): In part because the world around us is becoming “connected” through a growing number of IoT sensors, mobile devices, and the world’s affinity for the Internet, the sheer volume of information available is already staggering.

Daniel B. Kline (endless payment): While subscriptions have always been a factor on the enterprise side of the software business, they’re now moving into the consumer end of things. The leader has been Microsoft (NASDAQ:MSFT), which has managed to move a large part of its Office customer base into a subscription model. 

Tim Green (budget smartphones): Zenfone 2 from Asus and the Moto G from Motorola.

Deep learning and Wearables

RE.WORK Deep Learning Summit, Boston

May 26-27, 2015
Boston, Massachusetts

Internet of Things Summit, Boston 2015

May 28, 2015 – May 29, 2015

Hyatt Regency Boston, Boston, Massachusetts, USA

– See more at:

big data and education

Big Data is Finally Coming to Education Here’s What We’ve Learned So Far

Long lectures don’t work.

The best predictor of future course behavior is past course behavior.

Data from MOOCs suggest that one way to boost completion rates is to increase engagement early in the course.

Even in online courses, offline support is essential.


More IMS blog entries on Big Data:

big data history

A Bried History of BIG Data

Volume, Velocity, Variety

Business Intelligence

Internet of Things

privacy, security, intellectual property

mobile Internet


Super Mario gets artificial intelligence

Researchers create ‘self-aware’ Super Mario with artificial intelligence

A team of German researchers has used artificial intelligence to create a “self-aware” version of Super Mario who can respond to verbal commands and automatically play his own game.

Artificial Intelligence helps Mario play his own game

Students at the University of Tubingen have used Mario as part of their efforts to find out how the human brain works.

The cognitive modelling unit claim their project has generated “a fully functional program” and “an alive and somewhat intelligent artificial agent”.

Can Super Mario Save Artificial Intelligence?

The most popular approaches today focus on Big Data, or mimicking humansthat already know how to do some task. But sheer mimicry breaks down when one gives a machine new tasks, and, as I explained a few weeks ago, Big Data approaches tend to excel at finding correlations without necessarily being able to induce the rules of the game. If Big Data alone is not a powerful enough tool to induce a strategy in a complex but well-defined game like chess, then that’s a problem, since the real world is vastly more open-ended, and considerably more complicated.

free “big data” sources

The Free ‘Big Data’ Sources Everyone Should Know, click here.
US Census Bureau click here.
European Union Open Data Portal, click here., click here.
The CIA World Factbook, click here., click here.
NHS Health and Social Care Information Centre, click here.
Amazon Web Services public datasets, click here.
Facebook Graph, click here.
Gapminder, click here.
Google Trends, click here.
Google Finance, click here.
Google Books Ngrams, click here.
National Climatic Data Center, click here.
DBPedia, click here.
Topsy, click here.
Likebutton, click here.
New York Times, click here.
Freebase, click here.
Million Song Data Set, click here.

Big Data. Tracking Students’ Grades Minute-By-Minute: Help or Hindrance

Flanagan, L. (n.d.). Tracking Students’ Grades Minute-By-Minute: Help or Hindrance? MindShift. Retrieved May 12, 2014, from
For all the data and feedback they provide, student information systems interfere with learning.
“School isn’t about learning. It’s about doing well.”
The singular focus on grades that these systems encourage turns learning into a competitive, zero-sum game for students.
My notes:
the parallel with the online grades systems at K12 is the Big Data movement at Higher Ed. Big Data must be about assisting teaching, not about determining teaching and instructors must be very well aware and very carefully navigating in this nebulous areas of assisting versus determining.
This article about quantifying management of teaching and learning in K12 reminds me the big hopes put on technocrats governing counties and economies in the 70s of the last centuries when the advent of the computers was celebrated as the solution of all our problems. Haven’t we, as civilization learned anything from that lesson?