Searching for "algorithms"

social media and the devaluation of information

Iran’s blogfather: Facebook, Instagram and Twitter are killing the web

http://www.theguardian.com/technology/2015/dec/29/irans-blogfather-facebook-instagram-and-twitter-are-killing-the-web

is it possible that the Iranian government realized the evolution of social media and his respective obsolescence and this is why they freed him prematurely?

Blogs were gold and bloggers were rock stars back in 2008 when I was arrested.

The hyperlink was a way to abandon centralisation – all the links, lines and hierarchies – and replace them with something more distributed, a system of nodes and networks. Since I got out of jail, though, I’ve realised how much the hyperlink has been devalued, almost made obsolete.

Nearly every social network now treats a link as just the same as it treats any other object – the same as a photo, or a piece of text. You’re encouraged to post one single hyperlink and expose it to a quasi-democratic process of liking and plussing and hearting. But links are not objects, they are relations between objects. This objectivisation has stripped hyperlinks of their immense powers.

At the same time, these social networks tend to treat native text and pictures – things that are directly posted to them – with a lot more respect. One photographer friend explained to me how the images he uploads directly to Facebook receive many more likes than when he uploads them elsewhere and shares the link on Facebook.

Some networks, like Twitter, treat hyperlinks a little better. Others are far more paranoid. Instagram – owned by Facebook – doesn’t allow its audiences to leave whatsoever. You can put up a web address alongside your photos, but it won’t go anywhere. Lots of people start their daily online routine in these cul-de-sacs of social media, and their journeys end there. Many don’t even realise they are using the internet’s infrastructure when they like an Instagram photograph or leave a comment on a friend’s Facebook video. It’s just an app.

A most brilliant paragraph by some ordinary-looking person can be left outside the stream, while the silly ramblings of a celebrity gain instant internet presence. And not only do the algorithms behind the stream equate newness and popularity with importance, they also tend to show us more of what we have already liked. These services carefully scan our behaviour and delicately tailor our news feeds with posts, pictures and videos that they think we would most likely want to see.

Today the stream is digital media’s dominant form of organising information. It’s in every social network and mobile application.

The centralisation of information also worries me because it makes it easier for things to disappear.

But the scariest outcome of the centralisation of information in the age of social networks is something else: it is making us all much less powerful in relation to governments and corporations. Surveillance is increasingly imposed on civilised lives, and it gets worse as time goes by. The only way to stay outside of this vast apparatus of surveillance might be to go into a cave and sleep, even if you can’t make it 300 years.

big data

big-data-in-education-report

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

Suggestions:

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:

http://blog.stcloudstate.edu/ims/?s=big+data&submit=Search

Personalized Learning

Response: Personalized Learning Is ‘a Partnership With Students’

http://blogs.edweek.org/teachers/classroom_qa_with_larry_ferlazzo/2015/09/response_personalized_learning_is_a_learning_partnership_with_students.html

building relationships with students so I can better connect lessons to their interests, hopes and dreams; providing them with many opportunities for organizational and cognitive choice; and creating situations where they can get positive, as well as critical, feedback in a supportive way from me, their classmates and themselves.

Response: Personalized Learning Is ‘Based On Relationships, Not Algorithms’

http://blogs.edweek.org/teachers/classroom_qa_with_larry_ferlazzo/2015/09/response_personalized_learning_is_based_on_relationships_not_algorithms.html

Too often, the notion of “personalized learning” means choice-based programmed rather than truly personalized. This comes from the tech world, where “personalization” is synonymous with user choice. It’s the idea of giving a thumbs up or a thumbs down on Pandora. It’s the idea of having adaptive programs that change based upon one’s personal preferences. It’s the Facebook algorithm that tells you what information is the most relevant to you. It’s about content delivery rather than user creation.

While tech companies promise personalization, they often promote independent, isolated learning. True personalization is interdependent rather than isolated. True personalization is based upon a horizontal relationship rather than a top-down customization. True personalization is based upon a deeply human relationship rather than a program or an algorithm or a set of scripts. True personalization is a mix between personal autonomy and group belonging. It’s a mix between what someone wants and what someone needs. It’s a chance to make, rather than simply a chance to consume.

1 2 3 4