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Collective Intelligence & Distance Learning
Collective intelligence is a shared or group intelligence involving knowledge creation and flow. Pooled brainpower emerges from the collaboration and learning actions of a community of connected individuals empowered by social media, participatory tools, and mobile platforms.
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We need to use technology to get smarter about care

We need to use technology to get smarter about care | Collective Intelligence & Distance Learning | Scoop.it

According to the Institute for Alternative Futures, healthcare accounts for only 10-25% of the variance in health over time. The remaining variance is shaped by genetic factors (up to 30%), health behaviours (30-40%), social and economic factors (15-40%), and physical environmental factors (5-10%).

 

Too often, every stakeholder in the system views care through their own lens – the data they collect and the interventions they can sponsor. Doctors want to identify symptoms and treat them. Hospitals want to bring patients in for procedures that will cure them. Pharmaceutical companies want to find people who might benefit from their medication. Public health specialists want to cut the number of premature births or the incidence of diabetes. Social workers want to change harmful behaviours.

 

Unfortunately that information is scattered in various databases and departments, making it hard to achieve a holistic picture of the patient. Healthcare organisations can magnify their impact on individual health by dealing with issues beyond office visits and hospitalisations.

 

There's an opportunity to dramatically improve the care ecosystem (Smarter Care), making it more efficient, by applying analytics to data generated at every point in the care cycle. This phenomenon, known as big data, would develop a fuller understanding of individuals and the factors affecting their social and physical health.

 

Smarter Care systems have five common attributes:

 

• Intervention – Discovering the points in their lives where individuals can be influenced, and the most effective intervention strategy

 

• Knowledge – Assessing what has worked and applying that information to improving the system going forward

 

• Collaboration – Leading individuals to work with the right care-givers to make healthy choices or change their social determinants

 

• Coordination – Sharing care, knowledge and accountability across clinical and social boundaries

 

• Learning – Using analytics to study communities and understand who is at medical risk and how those risks are created, whether by medical, psychological or social factors

 

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Realtime Analytics For Education: Ontract Wants To Do For Student Data What Mint.com Did For Financial Data

Realtime Analytics For Education: Ontract Wants To Do For Student Data What Mint.com Did For Financial Data | Collective Intelligence & Distance Learning | Scoop.it

Many teachers and administrators believe that the fragmentation of educational platforms and opacity of student data is crippling the system. The last thing that education needs is more “comfortable” or “sexy” data silos with decent UX/UI, Ontract's founder, Julian Miller says. And while there are a lot of edtech platforms out there attempting to aggregate data, the founder believes the advanced analytics, collaboration element and personalized learning engine is what could give OnTract a leg up.

 

Ontract, a realt-time analytics platform for K-12 schools that aims to do for educational data what Mint.com did for financial data. Ontract’s reporting is designed to give teachers and administrators quick visibility into a range of information from individual students all the way to a federal level. Teachers can access standard reports generated by Ontract, create their own based on particular data subsets, view charts, graphs and infographics and tap into the startup’s predictive technology which aims to help them get a sense of what all this data means.

 

As to how it works: After schools register to use Ontract, the company connects the school’s core learning systems to its platforms and teachers can then connect their personal tools. The startup applies its analytics to identify trends, patterns or outliers, and notifies teachers when there’s a problem or aberration.

 

The startup’s personalized learning engine generates a playlist of resources to help, which teachers can then push to parents in the same way they’d respond to an email. And parents, in turn, have instant access to those resources to help their children in real-time, rather than having to wait for teachers to find time to walk them through it.

 

 

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Mendeley injects some pace into academia with fast, big data

Mendeley injects some pace into academia with fast, big data | Collective Intelligence & Distance Learning | Scoop.it
London startup Mendeley is already beloved by researchers around the planet for helping them manage their work. Now it's unveiled a new product that it hopes can help universities get a better handle on what's happening right now.

 

“The biggest problem in academia is the long waiting time: it can take three to five years from the time you have done research to get it published — all the decisions you make in an academic career are based around that time lag,” Victor Henning, Mendeley’s co-founder and CEO told me.

 

“We’ve developed a product that’s packaged into a data dashboard and allows universities to see what’s happening right now: what are the journals they’re reading? What are they not reading?”

 

It’s not just about optimizing efficiency by dropping unread journal subscriptions, or watching which areas are growing fast. The service can also let universities see the other side: which members of their faculty are publishing most? Who’s being cited? What areas are they active in? Those are things that institutions care deeply about — but struggle to find right now because most data out there is old data.

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Colleges Awakening to the Opportunities of Data Mining

Colleges Awakening to the Opportunities of Data Mining | Collective Intelligence & Distance Learning | Scoop.it
Netflix meets Google meets academia. Data mining is reshaping the college experience.

 

CAMPUSES are places of intuition and serendipity: a professor senses confusion on a student’s face and repeats his point; a student majors in psychology after a roommate takes a course; two freshmen meet on the quad and eventually become husband and wife. Now imagine hard data substituting for happenstance.

 

As Katye Allisone, a freshman at Arizona State University, hunkers down in a computer lab for an 8:35 a.m. math class, the Web-based course watches her back. Answers, scores, pace, click paths — it hoovers up information, like Google. But rather than personalizing search results, data shape Ms. Allisone’s class according to her understanding of the material.

 

With 72,000 students, A.S.U. is both the country’s largest public university and a hotbed of data-driven experiments. One core effort is a degree-monitoring system that keeps tabs on how students are doing in their majors. Stray off-course and a student may have to switch fields.

 

And while not exactly matchmaking, Arizona State takes an interest in students’ social lives, too. Its Facebook app mines profiles to suggest friends. One classmate shares eight things in common with Ms. Allisone, who “likes” education, photography and tattoos. Researchers are even trying to figure out social ties based on anonymized data culled from swipes of ID cards around the Tempe campus.

 

This is college life, quantified.

 

Read more of this article from The New York Times and The Chronicle of Higher Education.

 

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AppHero Raises $1.8 Million For App Recommendation Service Which Learns Your Interests From Facebook

AppHero Raises $1.8 Million For App Recommendation Service Which Learns Your Interests From Facebook | Collective Intelligence & Distance Learning | Scoop.it
Following Apple's acquisition of Chomp, the app search and discovery business continues to heat up.

 

App Hero is building an app recommendation service that helps users find new applications to try by analyzing their historical activity and their social data. It also includes a friending functionality so users can see which apps their friends are installing and recommending.

 

At first glance, this setup sounds very similar to what the mobile app recommendation platform Crosswalk is currently doing. Like AppHero, Crosswalk keeps track of your apps and allows you to recommend your favorite ones to others. And it does a better job at figuring out your interests than iTunes Genius does, in my opinion.

 

However, while Crosswalk taps into Facebook (and other services) to discover which of your friends are on the platform, AppHero takes a different route – it actually uses your Facebook data to help it make its recommendations.

 

A recent high school graduate, Satok started AppHero in May 2011, finding inspiration in the untapped potential of mobile technology. “These devices are capable of so much, and people are under-utilizing them,” he says. “I’ve been thinking about how we can deliver the most value to consumers, and it led down this path of thinking about the space of personalization.”

 

He thinks that AppHero is a better tool for app discovery than an app search site because search is only great when you already know what you’re looking for. “But people don’t know what they don’t know,” he says. As for the comparisons with Crosswalk and the like, he claims the big differentiator is the underlying intelligence in AppHero. Not only does the service understand “who someone is and what they’re like,” Satok says, it also learns more about them over time by tracking their Facebook social data (think: updated location, marital status, likes etc.) and activity (e.g., geo-location, app downloads and likes/dislikes).

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Desire2Learn’s New Learning Suite Aims To Predict Success, Change How Students Navigate Their Academic Career

Desire2Learn’s New Learning Suite Aims To Predict Success, Change How Students Navigate Their Academic Career | Collective Intelligence & Distance Learning | Scoop.it

To do this, Desire2Learn wants to bring predictive analytics into play in education. But why? Well, first and foremost because, today, if students want to figure out whether a course is right for them — or how well they might perform in that course — they’re hard pressed to find a good answer. They can ask fellow students, check websites that rank faculty based on nebulous criteria or try to find surveys, but none of these options are ideal.

 

With its new analytics engine, Desire2Learn aims to change that by giving students the ability to predict their success in a particular course based on what they’ve studied in the past and how they performed in those classes. The new, so-called “Student Success System,” was built (in part) from the technology it acquired from Degree Compass; however, while Degree Compass used predictive analytics to help students optimize their course selection, the new product aims to help both sides of the learning equation: Students and teachers.

 

On the teacher side, Desire2Learn’s new analytics engine allows them to view predictive data visualizations that compare student performance against their peers so that they can identify at-risk students, for example, and monitor a student’s progress over time.


The idea is to give teachers access to important insight on stuff like class dynamics and learning trends, which they can then combine with assessment data, to improve their instruction or adapt to the way individual students learn. In theory, this leads not only to higher engagement, but also better outcomes

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Essentially, the tool allows students to move their academic resume to the cloud so they can take it with them after they graduate, which the company is incentivizing by offering 2GB of free storage.

 

Basically, what we’ve come to realize, the Desire2Learn CEO tells me, is that the company’s initial approach to business (or academic) intelligence was off track. “Students and teachers don’t necessarily want more data, they want more insight and they want that data broken out in a way that they can understand and helps them more quickly visualize the learning map,” he says.

 

When I asked if building and adding more and more tools and features would dilute the experience and result in feature overload, Baker said that the company doesn’t want to build a million different tools. Instead, it wants to become a platform that supports a million tools and allows third-parties that specialize in particular areas of education to help develop better products.

 

Through open-sourcing its APIs, Desire2Learn along with Edmodo and an increasing number of education startups are beginning to tap into the potential inherent to the creation of a real ecosystem. Adding predictive analytics tools gives Desire2Learn another carrot with which they hope to be able to draw both teachers, students and development partners into its ecosystem.

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Academia.edu Adds Analytics To Bring Transparency To How Research Spreads

Academia.edu Adds Analytics To Bring Transparency To How Research Spreads | Collective Intelligence & Distance Learning | Scoop.it
Far away from Silicon Valley is another echo chamber in the Ivory Tower, except there's very little transparency there about how content and ideas spread.

 


Academia.edu, a social network for researchers, just unveiled an analytics dashboard that’s meant to help scientists and other academics understand how their work is being read and distributed. It’s a difference from an older, more opaque world in which researchers vied to get into elite journals like the New England Journal of Medicine.

 

“To be a successful academic, it’s becoming as important to have an established web presence as it is to be published in a journal and it’s going to be increasingly critical,” said CEO Richard Price.  To those of us in the tech community, the concept of an analytics dashboard would appear pretty basic. But in the slow-moving world of academia, Price says it has profound consequences.

 

“Hiring and grant committees know that there is this credit gap where papers are being read, but they haven’t had the metrics to prove that historically,” Price said. “They’d look at which journal you published in and your citations.”

 

At the same time, it can take citations five years to emerge, he added. Real-time metrics that track mentions on the web and on Twitter could give credibility to a researcher a lot sooner.

 

“It gives scientists visibility into all of the traffic they’re receiving by country and other factors. It’s super granular and it’s in real-time,” Price said. A professor, for example, will be able to see how many paper downloads they’ve gotten in the last 30 days.

 

Price said the metrics also preserve reader privacy and are basically in line with what other analytics products offer like geographic data and time spent on the page. 

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Percolate's latest brew aims to make brands better social curators, content creators

Percolate's latest brew aims to make brands better social curators, content creators | Collective Intelligence & Distance Learning | Scoop.it
New York startup Percolate, which helps brands sort and curate content to share on Facebook, Twitter and other social channels, has released its newest version.

 

The startup, which helps brands act like savvy curators of the social Web, launched last year with a platform that filters through hundreds of sources of content, bubbles up the most relevant pieces and then lets brands share them on Facebook, Twitter and other social channels.

 

But the company’s newest version, which it released to clients last week, goes beyond the curation of content from other sources to making it easier for brands to tap into their own reserves of digital content. In addition to indexing and tagging brands’ owned content, a few other updates include enhancing the algorithms that recommend the best content, providing extra monitoring and notifications around the performance of posts, and automatic optimizing for the different social platforms.

 

“The biggest challenge brands have is, ‘what should I be talking about now?,’” said Percolate co-founder James Gross. “It’s not audience acquisition – you can buy followers – and distribution is also taken care of [with Facebook’s reach generator, for example]… The challenge is what content do I create right now.”

 

To help a brand figure out the kinds of content that it should be sharing, Percolate maps the brand’s “interest graph” against more than six million sources and finds the references, which can include from 500 to 1,500 sources, that are unique to each brand. From there, Percolate’s algorithms consider a number of variables, including recency, popularity, authority and, most recently, keywords, to suggest the different content that a brand’s social editor should share everyday.

 

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Bye bye Babel: Breaking language barriers online

Bye bye Babel: Breaking language barriers online | Collective Intelligence & Distance Learning | Scoop.it
According to the translation firm Smartling, native English speakers only represented 3% of the total Internet population in 2011. Yet, 56% of online pages are English-only.

 

So how do we break language barriers online? Well, here are a few tools that can help you browse content in a language you don’t speak – pages of course, but also video and even speech.

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Harnessing Collective Intelligence in Decision Making through Big Data Analytics - Smart Data Collective

Harnessing Collective Intelligence in Decision Making through Big Data Analytics - Smart Data Collective | Collective Intelligence & Distance Learning | Scoop.it
Harnessing Collective Intelligence in Decision Making through Big Data AnalyticsSmart Data CollectiveDecision! Decision! Decision! What a hazardous and difficult human endeavor it is!

 

Decision! Decision! Decision! What a hazardous and difficult human endeavor it is! Those of us who had to make decisions in personal life, business or profession know that the chance of our decision producing the desired end-result is always in doubt. This is so mainly because decision made today fructify tomorrow. If all decision makers were clairvoyant no one would make wrong decisions – making decisions would be a routine job.

 

Unfortunately this is never going to happen. We will keep making wrong decisions as we do today. However, we could enhance, in a substantial way, our chances of making the right decision by reviewing everything that is happening now or has happened in the past relating to our target area of activity. Of course, this may be a tall order to follow but in the information age that we are living in, it seems feasible. There is so much data available at diverse sources that if we evolve a scientific and feasible way of disseminating all this data we will find answers to our queries as we have never been able to do so before. The ‘way’ we mentioned above is the ‘Big Data Analytics’.

 

Genesis of Big Data

 

Big Data, one of the hottest IT buzzwords of 2012, has emerged as a new technology paradigm to address the volume, velocity and variability of massive data coming from different sources. The social media is one well known source of big data. A somewhat less known source but big nonetheless is the data generated by data acquisition systems (DAS) in machinery and structures in the field of engineering. Large volumes of data are also being generated by health monitoring devices of interest to medical professionals. There are (many) other sources too. Within this heaps of massive data, there is treasure of information that can be extracted for saving major disasters, accidents, outbreak of epidemics, etc. In the field of business and marketing, big data available through the ‘social network’ is already being proactively used in propelling growth of businesses.

 

Businesses have been relying on tools such as Business Intelligence (BI) dashboards and reports for decisions based on transactional data stored in relational databases. With evolution of social media, we started seeing emergence of non-traditional, less structured data such as weblogs, social media feeds, email, sensors, photographs and YouTube videos that can be analyzed for useful information. With reduction of cost in both storage and compute power, it is now feasible to store and analyze this data, as well, for meaningful purposes. As a result, it is important that businesses cast a new look at the extended range of data, i.e. Big Data, for business intelligence and for decision making.

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