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Learning analytics at Stanford takes huge leap forward with MOOCs

Learning analytics at Stanford takes huge leap forward with MOOCs | Assessment of Deeper Learning | Scoop.it
Stanford's Lytics Lab gathers data from massive open online courses to learn more about how we learn. The group studies student behavior to measure interaction and performance.

Via Susan Bainbridge
davidgibson's insight:

Without scanning the whole article, what do you think we can do to dive deeper into use patterns? That is, how can we go deeper than what is mentioned in this quote:

 

"They found that people take classes or stop for different reasons, and therefore referring globally to "dropouts" makes no sense in the online context. They identified four groups of participants: those who completed most assignments, those who audited, those who gradually disengaged and those who sporadically sampled. (Most students who sign up never actually show up, making their inclusion in the data problematic.) The point of all this is not simply to record who is doing what but to "provide educators, instructional designers and platform developers with insights for designing effective and potentially adaptive learning environments that best meet the needs of MOOC participants," the researchers wrote."

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Marci Segal, MS's comment, April 13, 2013 7:56 AM
Good to have a peek inside - thanks!
Marci Segal, MS's curator insight, April 13, 2013 7:57 AM

Good to have a peek inside what's going on, eh?  Ready to take the plunge?

Assessment of Deeper Learning
AI, machine learning, MOOCs (oh my!) and more...anything that strikes me as potentially related to digital media-based assessment of higher order thinking.
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Learning to Adapt Research : Education Growth Advisors

Learning to Adapt Research : Education Growth Advisors | Assessment of Deeper Learning | Scoop.it
- Insight into the variability of adaptive learning solutions, based on their Approach to adaptivity and selected Taxonomy and Maturity attributes introduced and defined- A framework for how an organization might use a set of contextual considerations and instructional objectives to determine a best-fit approach to adaptive learning- Detailed profiles of eight adaptive learning suppliers and a list of other organizations to watch
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...a framework for thinking about adaptive learning in higher education

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e-Book: Machine Learning and Recommendation Engine

e-Book: Machine Learning and Recommendation Engine | Assessment of Deeper Learning | Scoop.it
Practical Machine Learning: Innovations in Recommendation

Ted Dunning & Ellen Friedman
Building a simple but powerful recommendation system is much easier than you think.
davidgibson's insight:

Free book to download...

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Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms - The Journal of Physical Chemistry B (ACS Publications)

Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms - The Journal of Physical Chemistry B (ACS Publications) | Assessment of Deeper Learning | Scoop.it
Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms - The Journal... http://t.co/e0610dcLRI
davidgibson's insight:

These researchers report how they 'simulate first, then test in the real world' to find new self-assembly pathways

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Resources | Society for Learning Analytics Research

Below you will find suggestions of introductory courses, lectures and articles on Learning Analytics.


Via Elizabeth E Charles
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9 "must read" articles – Data Science Central

9 "must read" articles – Data Science Central | Assessment of Deeper Learning | Scoop.it

Vincent Gran

davidgibson's insight:

Vincent Granville has done would-be data scientists a big favor by putting these 9 articles together

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Model-based machine learning

davidgibson's insight:

Its great to have articles like this to help introduce subject matter experts to a new world of analysis. Great read.

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Deniz Yuret's Homepage: Machine learning in 10 pictures

Deniz Yuret's Homepage: Machine learning in 10 pictures | Assessment of Deeper Learning | Scoop.it
davidgibson's insight:

10 pictures of data worth keeping in mind. Great lessons! Passes the Guido Sarducci test for university.

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Machine Learning Video Library - Learning From Data (Abu-Mostafa)

Machine Learning Video Library - Learning From Data (Abu-Mostafa) | Assessment of Deeper Learning | Scoop.it
Video lectures from Caltech's Machine Learning course http://t.co/P51MGGbL6E provide clear, simple, & accessible description of ML concepts.
davidgibson's insight:

Don't miss this pdf: http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Could we please have a whole world full of people like this author?

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Machine learning branches out - MIT News Office

Machine learning branches out - MIT News Office | Assessment of Deeper Learning | Scoop.it
An algorithm that extends an artificial-intelligence technique to new tasks could aid in analysis of flight delays and social networks.
davidgibson's insight:

Here is a great and simple explanation of a new network algorithm. I believe that another form of amplification of results that I seem to have seen concerns the overfitting problem. If I select a solution from a range and just chase after the highest or best criteria (of any sort: correlation, max error, r-squared, mean error, or more complex criteria) then the predictor equation seems to be over confident.

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Machine Learning Lesson of the Day – Supervised and Unsupervised Learning | StatsBlogs.com | All About Statistics

Machine Learning Lesson of the Day – Supervised and Unsupervised Learning http://t.co/FwmvPNVP9c
davidgibson's insight:

This clarified something I had confused for a while. I always thought that supervised machine learning was with humans in the loop, but according to this definition, it is just when there is a search on for an explanitory relationship to explain some y in terms of some array of x's. I guess the supervision can then also be the fitness criteria.

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Data Base vs. Data Science

Data Base vs. Data Science | Assessment of Deeper Learning | Scoop.it
One thing which Big Data certainly made happen is that it brought the database/infrastructure community and the data analysis/statistics/machine learning communities closer together.

Via Carla Gentry CSPO
davidgibson's insight:

Get ready for the jump to hyperspace...

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Carla Gentry CSPO's curator insight, September 9, 2013 8:59 AM

The machine learning community, on the other hand, has it’s root in linear algebra and probability theory. Objects are usually encoded as a feature vector, that is, a list of numbers describing different properties of an object. Data is often collected in matrices where each row corresponds to an object, and each column to a feature, not much unlike a table in a database.

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Forget your fancy data science, try overkill analytics

Forget your fancy data science, try overkill analytics | Assessment of Deeper Learning | Scoop.it
Carter S. won his first-ever Kaggle competition — our own GigaOM WordPress Challenge — using a brute force method of data science he calls overkill analytics.
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Brute force is not so hard if the brute is a parallel computer...

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Progress Report: NGLC Learning Analytics Projects | EDUCAUSE.edu

Progress Report: NGLC Learning Analytics Projects | EDUCAUSE.edu | Assessment of Deeper Learning | Scoop.it
Think of learning analytics as the warning lights of online learning's dashboard – that gleaming assemblage of dials and icons that alerts a driver at once if gas in the tank is running low, a door is open, or the cooling system ...
davidgibson's insight:

It's great to have multiple projects to scan over and learn from. The one thing I would say is that learning analytics is a term that is being stretched to mean more than learning (and that is fine until some new name comes along). I think the best practice will be to talk about big data analytics for learning, retention, recruitment, alumni networks, business practices as all part of a new wave of using near real time analysis methods on big data sets to better understand and model learners.

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The Great Adaptive Learning Experiment -- Campus Technology

The Great Adaptive Learning Experiment -- Campus Technology | Assessment of Deeper Learning | Scoop.it
Higher ed institutions around the globe are exploring the potential of adaptive technology to revolutionize the way students learn.
davidgibson's insight:

I disagree slightly in the sense that adaptation is (I think) the only way to personalize 'at scale.' so it is not just one of a set of methods to personalize, but the only realistic method to use when going to scale.

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What has happened in theoretical machine learning in the last 5 years (2009-2014)?

What has happened in theoretical machine learning in the last 5 years (2009-2014)? | Assessment of Deeper Learning | Scoop.it

Yisong Yue's answer: 

The theoretical contributions that first come to mind are methods for (near-)optimally estimating latent factor models...and much more in this great posting on Quora

davidgibson's insight:

Great short guide to a lot of related resources in an expert's lit search.

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NuPIC Details

NuPIC Details | Assessment of Deeper Learning | Scoop.it

The video demo here shows examples of temporal pattern learning by the NuPIC algorithm

davidgibson's insight:

This NuPIC video demo from 2013 shows an example of online machine learning of temporal patterns

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Bigdata-Techniques | Big Data Projects

Bigdata-Techniques | Big Data Projects | Assessment of Deeper Learning | Scoop.it

Big data techniques and terms list

davidgibson's insight:

Here is a helpful start at a dictionary. The visualization examples are just three of many...

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New U.N. Report: Climate Change Risks Destabilizing Human Society

New U.N. Report: Climate Change Risks Destabilizing Human Society | Assessment of Deeper Learning | Scoop.it
In a new U.N. report released on Monday morning (Japan time) scientists come to a stark conclusion: Unless the world changes course immediately and dramatically, the fundamental systems that support human civilization are at risk. The Intergovernmental Panel on Climate Change’s new report—which is seven years in the making—draws on...
davidgibson's insight:

Sorry to be a bit off topic, but this report is one in a long series of highly consistent messages that the world seems to be ignoring.

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25 Awesome Open Online Educational Resources – Videos, Ideas, Materials, Books and More - TechnoDuet

25 Awesome Open Online Educational Resources – Videos, Ideas, Materials, Books and More - TechnoDuet | Assessment of Deeper Learning | Scoop.it

These resources are mostly open, free, often downloadable as pdf, ePub, or other formats and, are quite valuable in terms of the contents.

 

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free is good

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How to Define Your Machine Learning Problem | Machine Learning Mastery

How to Define Your Machine Learning Problem | Machine Learning Mastery | Assessment of Deeper Learning | Scoop.it
The first step in any project is defining your problem. You can use the most powerful and shiniest algorithms available, but the results will be meaningless if
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How Can Educational Data Mining and Learning An...

How Can Educational Data Mining and Learning An... | Assessment of Deeper Learning | Scoop.it
Check out this beautiful infographic which answers why would colleges want to have analytics, how can educational data mining and learning analytics improve and personalize education, and how does this process work.
davidgibson's insight:

An infrographic to share with an educational  data science team

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How To Build A Successful Data Science Team - InformationWeek

How To Build A Successful Data Science Team - InformationWeek | Assessment of Deeper Learning | Scoop.it
How To Build A Successful Data Science Team InformationWeek The second data science role is that of machine-learning expert, a statistics-minded person who builds data models and makes sure the information they provide is accurate, easy to...
davidgibson's insight:

For a learning organization, I think we need to add a learning scientist and a games and simluations developer/researcher to make an all-around team.

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Scott Nicholson: Meaningful Gamification: Motivating through Play instead of Manipulating through Re

Gamification is the use of game design concepts to create a layer on a real world setting. Typical gamification focuses on the use of rewards like points and badges to change the behavior of users, which can cause long-term damage to intrinsic motivation. Meaningful gamification is the use of design concepts from games and play to help people find personal connections to a real-world setting.

http://gamelab.mit.edu/event/guest-lecture-scott-nicholson/

 

http://www.twitch.tv/mitgamelab/c/3383085&utm_campaign=archive_export&utm_source=mitgam­elab&utm_medium=youtube


Via Kim Flintoff
davidgibson's insight:

make it real

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Data Base vs. Data Science

Data Base vs. Data Science | Assessment of Deeper Learning | Scoop.it
One thing which Big Data certainly made happen is that it brought the database/infrastructure community and the data analysis/statistics/machine learning communities closer together.
davidgibson's insight:

As a self-taught database guy, I might need a spaceship to make this jump to hyperspace...

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A Course in Machine Learning

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A free online book...

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