Learning Analytics

Por qué usar Data mining?

Another visualization tool

Knowledge Cartography: Software Tools and Mapping Techniques. (Eds.) Okada, A., Buckingham Shum, S. and Sherborne, T. Springer: Advanced Information and Knowledge Processing Series. ISBN: 978-1-84800-148-0"

According to Eva Tardos, Jacob Gould Schurman Professor of Computer Science at Cornell University, the researchers' paper "is more the beginning of research than a definitive result or end product." The researchers' models make several simplifying assumptions - including the number of competitors - that make the math easier but limit their applicability. Nonetheless, "just raising the questions is an important step forward," Tardos says.

Is Goodhart's law relevant when thinking about Learning Analytics

Several weeks ago, I posted a question on Quora asking for the best data blogs. There have been 26 replies with dozens of blogs recommended.

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data.

Hace Estadísticas de tu Cuenta.

De hecho cualquier persona puede poner el nombre de otra y observar las estadísticas de comportamiento de esa otra persona. Es por tanto, una herramienta de doble filo, permite comparar tu actividad con la de otros, pero por otra parte están controlando a qué horarios te conectas, a quien retuiteas, cuales son las palabras que más utilizas, etc. A mí personalmente me ha sorprendido mis propias acciones. A veces piensas que vas en una dirección y sin embargo estás performando otra. Si alguna persona lo prueba, me gustaría saber su opinión. Saludos

NetMiner is an innovative software tool for Exploratory Analysis and Visualization of Network Data. NetMiner allows you to explore your network data visually and interactively, and helps you to detect underlying patterns and structures of the network

And I'm saying the semantic web won't work. Can't work.

generating word clouds, interesting as a game, as analysis can be very simple, depending on what you want to search.

You know your data is big - you found Hadoop. What implications must you consider when working at this scale? This lecture addresses common challenges and general best practices for scaling with your data.

Have been listening to a Dave Snowden podcast of a "101 organic KM course". Amongst many familiar themes is the mention of the pitfalls of too much transparency hurting innovation.

After visualizing chat room dialogue earlier this week, I thought it might be neat to try and visualize some Twitter dialogue from the #LAK11 course.  For some reason I thought it would be simple to extract tweets from Twitter for analysis.  It wasn't!  So if you happen to know an easier way please leave a comment.

My take on this week's readings and webinar

The majority of the information that we use in our daily lives is "dumb", or unconnected. The next step is "linked data", or data that talks to each other.

The availability of new sets of data has changed the way we live our lives: here are 10 examples of data which have changed everything from how we assess wars to how companies deliver milk.

Infochimps receives thousands of visitors every week hungry for interesting data. Unlock the value of your data by selling it on our open platform. Or put it up for free for public benefit, and get exposure for your work.

Goodhart's law states that once a social or economic indicator or other surrogate measure is made a target for the purpose of conducting social or economic policy, then it will lose the information content that would qualify it to play such a role.