#FakeNews #DigitalRecommendationEngines interpretation of data, market dependency “stupid smart recommendation engines” monopolistic structure, keep competitiveness, big data, market concentration
Reinventing Capitalism in the Age of Big Data (Basic Books / Hachette, 2018) by Viktor Mayer-Schönberger and Thomas Ramge.
more on this broad topic in this IMS blog:
and in the LIB 290 blog:
Computational Propaganda: Bots, Targeting And The Future
February 9, 201811:37 AM ET ADAM FRANK
Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
more on big data in this IMS blog
more on bots in this IMS blog
more on fake news in this IMS blog
New Report Examines Use of Big Data in Ed
By Dian Schaffhauser 05/17/17
new report from the National Academy of Education “Big Data in Education,” summarizes the findings of a recent workshop held by the academy
three federal laws: Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA) and the Protection of Pupil Rights Amendment (PPRA).
over the last four years, 49 states and the District of Columbia have introduced 410 bills related to student data privacy, and 36 states have passed 85 new education data privacy laws. Also, since 2014, 19 states have passed laws that in some way address the work done by researchers.
researchers need to get better at communicating about their projects, especially with non-researchers.
One approach to follow in gaining trust “from parents, advocates and teachers” uses the acronym CUPS:
- Collection: What data is collected by whom and from whom;
- Use: How the data will be used and what the purpose of the research is;
- Protection: What forms of data security protection are in place and how access will be limited; and
- Sharing: How and with whom the results of the data work will be shared.
Second, researchers must pin down how to share data without making it vulnerable to theft.
Third, researchers should build partnerships of trust and “mutual interest” pertaining to their work with data. Those alliances may involve education technology developers, education agencies both local and state, and data privacy stakeholders.
Along with the summary report, the results of the workshop are being maintained on a page within the Academy’s website here.
more on big data in education in this IMS blog
Big Data като Big Success
Анализът на масивите данни може да помогне на редица бизнеси да решават проблеми и да намаляват загубите и пропуснатите ползи, твърди Александър Ефремов
more on big data in this IMS blog
What can the government do about big data fairness?
At a Ford Foundation conference dubbed Fairness by Design, officials, academics and advocates discussed how to address the problem of encoding human bias in algorithmic analysis. The White House recently issued a report on the topic to accelerate research into the issue.
The FTC released two studies on how big data is used to segment consumers into profiles and interests.
U.S. CTO Megan Smith said the government has been “creating a seat for these techies,” but that training future generations of data scientists to tackle these issues depends on what we do today. “It’s how did we teach our children?” she said. “Why don’t we teach math and science the way we teach P.E. and art and music and make it fun?”
“Ethics is not just an elective, but some portion of the main core curriculum.”
more on big data in this IMS blog:
Higher Ed Can Be a One-Two Punch
According to a recent survey, many colleges lack critical analytics skills to effectively leverage data.
More on analytics and big data in this IMS blog:
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.
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:
Researchers use an app to predict GPA based on smartphone use
Dartmouth College and the University of Texas at Austin have developed an app that tracks smartphone activity to compute a grade point average that’s within 0.17 of a point.
More on Big Data in education in this blog:
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: