Notes On "Global Guidelines: Ethics in Learning Analytics"

Stephen Downes: Half an Hour

In my post referencing the ICDE's Global Guidelines: Ethics in Learning Analytics yesterday I said: This document summarizes the considerations of an ICDE working group on learning analytics. The presumption throughout is that a global ethic in learning analytics is needed, can be known, and comprehends the issues in this document. Definition and purpose of the use of Learning Analytics Where is data drawn from? What data must be included in learning analytics?

Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

T hey eventually discovered that all event-data representing human activity contain a special set of social activity patterns regardless of what the data is about.

SXSW: What Social Media Analytics and Data Can’t Tell You

Beth Kanter

I’m just back from the SXSW Interactive Festival where I was on a panel called “ What Social Media Analytics Can’t Tell You ” moderated by Alexandra Samuel of Vision Critical , Jeremiah Owyang , Crowd Companies, and Colby Flint, Discovery Channel.

Pattern Recognition, Neoterics and moving on.

Mark Oehlert

We have analytics out the YinYang for Twitter, Facebook and every other network we're on. Now I'm thinking about my fav passage from William Gibson, @GreatDismal, and his book Pattern Recognition : “We have no idea, now, of who or what the inhabitants of our future might be. Pattern recognition” . Between Neoterics and Pattern Recognition.we Like a lot of people, I read Anil Dash's " The Web We Lost " and I read Hugh MacLeod's corollary piece.

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding. And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. .

Social Physics and Cybercrime Detection

Irving Wladawsky-Berger

Social physics is based on the premise that all event-data representing human activity, - e,g,, phone call records, credit card purchases, taxi rides, web activity, - contain a special set of group behavior patterns.

Data 183

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

Irving Wladawsky-Berger

Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding. For the vast majority of the use cases, 69%, the key value of machine learning is to improve performance beyond that provided by traditional analytic techniques. And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. .

The social enterprise?

George Siemens

We need better analytics that can reveal patterns and connections in information abundance that we may have missed. We need technologies that can automatically detect and raise the profile of interesting patterns within an information stream. An event collapses the information trail into a state of patterns that seem obvious. Next week, I’ll be at the Social Business Forum. I’m looking forward to meeting up again with the innovative folks at Open Knowledge.

Deep Learning: Is it Approaching a Wall?

Irving Wladawsky-Berger

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks. The data requirements for deep learning are substantially different from those of other analytic methods in a number of dimensions.

January 2014Nonprofit Blog Carnival: Measurement and Learning

Beth Kanter

Want Useful Analytics? Tools fall into one of three categories – content analysis, surveys, or analytics. Marlene Oliveira, Copywriter and Communications Consultant, shares an interview with Jason Shim, “ Google Analytics Basics for Nonprofits: Interview with Jason Shim.”

The domain of disorder (ii)

Dave Snowden

In the domain if disorder people feel comfortable, they make decisions based on the habituated patterns of their past decision making, avoiding failure; sometimes repeating success. Experts tend to complain they need more time and resource to use their analytical skills.

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Cognitive insight projects take AI to the next level, using machine learning and other advanced algorithms to detect patterns in vast volumes of data. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.

Data 207

I Learned A Lot At My First-Ever F Up Nights

Dan Pontefract

Don’t just look for patterns, dig deeper into the characters you are inheriting as part of the acquisition.” His disdain for analytics, however, ended up costing him and the company dearly. Don’t shoot the messenger! I didn’t make up the group name.

Strata Conference Wrap Up: Big Data, Big Opportunities

George Siemens

I’ve had a bit of trouble carving out time to write concluding thoughts about the event (time deficiency is mainly due to last minutes activities related to our Learning and Knowledge Analytics Conference ). Blackboard Analytics : “Transforming data into actionable information.

Data 204

Emergent & Semantic Learning

Clark Quinn

However, as you track outcomes, e.g. success on practice, and start looking at that by doing data analytics, you can start trolling for emergent patterns (again, made up). Which helps explain the growing interest in analytics.

Why Do We Need Data Science when We’ve Had Statistics for Centuries?

Irving Wladawsky-Berger

One of the best papers on the subject is Data Science and Prediction by Vasant Dhar , - professor in NYU’s Stern School of Business and Director of NYU’s Center for Business Analytics , - which was published in the Communications of the ACM in December, 2013.

Preparing Students for an Increasingly Complex Business World

Irving Wladawsky-Berger

Universities generally do a pretty good job when it comes to teaching hard skills, - engineering methods, technology, analytical tools, finance, marketing, and so on. Are engineering and management schools adequately preparing students for our fast-changing, highly complex business world?

Determinism, Best Practice, and the ‘Training Solution’

Charles Jennings

Cynefin is a sense-making model – where patterns emerge from the information and data – that explains how to respond to ordered and disordered systems. Where good practice can be defined, training and development may also help by building better analytical capability and judgement.

Researching open online courses

George Siemens

What are the habits and patterns of learner self-organization in open online courses? Established researchers will likely already have some existing research techniques (such as social network analytics, discourse analytics, natural language processing, concept development, AI, and so on). In fall, we (TEKRI, NRC, UPEI and possibly a few other organizations) are hosting an open online course.

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The Top Ten Emerging Technologies of 2017

Irving Wladawsky-Berger

Precision Farming Increases Crop Yields - “Combining sensors and imaging of every plant with real-time data analytics improves farm outputs and reduces waste”. As new data is ingested, the system rewires itself based on whatever new patterns it now finds.

The 6 capabilities that drive future business value from Staggeringly Enormous Data

Ross Dawson

Emergent patterns and resultant value cannot always be anticipated so the key is developing consistent, efficient gathering of data that could drive better business decisions or operations today or down the track. Data analytics.

Data 252

Anticipating the Future of the IT Industry

Irving Wladawsky-Berger

While there’s no guarantee that historical patterns will continue to apply going forward, they might well be our most important guides as we peer into an otherwise unpredictable future. These early analytics applications dealt mostly with structured information.

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Cognitive insight projects take AI to the next level, using machine learning and other advanced algorithms to detect patterns in vast volumes of data. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.

Data 157

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

Irving Wladawsky-Berger

A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture.”. AI is now seemingly everywhere.

Data 209

AI - the Creation of a Human-Centric Engineering Discipline

Irving Wladawsky-Berger

Machine and deep learning are the latest examples of tools like the World Wide Web, search and analytics that are helping us cope with and take advantage of the huge amounts of information all around us. AI is rapidly becoming one of the most important technologies of our era.

Data 137

Serious AI Challenges that Require Our Attention

Irving Wladawsky-Berger

Police agencies hope to do more with less by outsourcing their evaluations of crime data to analytics and technology companies that produce predictive policing systems. Equating locations with criminality amplifies problematic policing patterns.”.

Narratives of culture: the score

Dave Snowden

The simplest is to look at the pattern revealed by the overall positioning of the narratives on one of the signifiers. This is going to be a little out of sequence, but I want to come to how we gather narrative for the new culture scans in future posts.

The Unmet Need for Trusted Talent Advisors

John Hagel

And there’s a growing amount of technology, loosely grouped into the Internet of Things, artificial intelligence and data analytics categories, that provide an opportunity for trusted talent advisors to gain growing insight into who we are, what we’re doing and what we’re accomplishing. In a world that’s changing ever more rapidly, we all need trusted advisors. It’s a significant unmet need that creates a very attractive business opportunity.

practice, creativity, and insight

Harold Jarche

“The machine versus human debate has actually divided big data analytics experts into two camps. Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

Design principles in DSS software

Dave Snowden

Now Jeff and I provide different perspectives on the whole issue of how you find patterns, my emphasis on human metadata compliments his ability to mash the numbers. Both are important, but there is too great a tendency to want to black box analytics.

The future of higher education and other imponderables

George Siemens

The patterns of change in higher education are surprisingly similar and global. Not all countries are adopting the aggressive UK model, but tuition patterns internationally (.pdf)

Data-Driven Decision Making: Promises and Limits

Irving Wladawsky-Berger

There are quite a number of interesting cases where people applied data analysis to uncover patterns that were useful in dealing with an unexpected new situation. The ordered world is the world of facts-based management; the unordered world represents pattern-based management.” .

Data 242

Reading and writing on the iPad

Jay Cross

Book reading strengthened our analytical skills, encouraging us to pursue an observation all the way down to the footnote. Screen reading encourages rapid pattern-making, associating this idea with another, equipping us to deal with the thousands of new thoughts expressed every day.

intangible value

Harold Jarche

Value network modeling and analytics reflect the true nature of collaboration with a systemic human-network approach to managing business operations. You can’t plan networks or force fit them into any pattern.

Where's the Money? The Future of the Mobility Ecosystem

John Hagel

And the more other people they know, the more helpful they can be to you because they can start to see patterns of movement among people like you. Within each of these domains, relevant data is siloed and we lose much of the value because of the inability to aggregate the data and mobilize a growing range of analytic tools to generate insight from the data.

Data 118

The Continuing, Transformative Impact of IT

Irving Wladawsky-Berger

The social matrix, the Internet of all things, big data and advanced analytics, and realizing anything as a services are the report’s top four trends. SMAC - Social, Mobile, Analytics & Cloud, - has become the new plastics , capturing the future of IT in one word, or rather, one acronym.

Data 255

Relationships and Dynamics - Seeing Through New Lenses

John Hagel

  Individual idiosyncrasies definitely play a role, but broader patterns of perception are at work as well. Are certain patterns of perception more or less helpful in these rapidly changing times?    Contemporary economics is largely built around equilibrium models that are essential if the detailed econometric analytics are to work. Yet, we do not have very good lenses or analytic tools to bring these dynamics to the forefront.

Human-AI Decision Systems

Irving Wladawsky-Berger

Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. . Sports analytics are now used by just about every professional sports team in the world.

System 147

The Fourth Industrial Revolution

Irving Wladawsky-Berger

Product enhancements - Technology advances are giving rise to a large variety of smart connected products and services, combining sensors, software, data, analytics and connectivity in all kinds of ways.

Follow the Data to Find the Money

John Hagel

Sure, technology is a key enabler of data capture, aggregation, and analytics, but the providers of this technology will not capture the vast bulk of the value – it will be those who have access to this data and, most importantly, those who can creatively find ways to generate economic value from this data. Then I could start to see patterns emerging in terms of what kinds of actions yielded what kinds of results in what kinds of environments.

Data 124

The Emerging Data Economy

Irving Wladawsky-Berger

Physicists, astronomers, biologists, and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining , for discovering patterns and extracting insights from all that data.

Data 204

Libary Lab funds library innovation projects

David Weinberger

The Library Innovation Lab [ blog ] that Kim Dulin and I co-direct had a few of its proposals accepted: Library Analytics Toolkit : Tools to enable libraries to understand, analyze, and visualize the patterns of activities, including checkouts, returns, and recent acquisitions, and to do so across multiple libraries. Harvard’s new Library Lab has announced the first projects it will be funding. It’s an exciting set of projects.

Can AI Help Translate Technological Advances into Strategic Advantage?

Irving Wladawsky-Berger

In their view business strategy consists of a series of highly interrelated conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction.