Tuesday, January 25, 2011

LAK-11 Further forays into big data and critiques of learning analytics

There are a few resources I found while looking at big-data articles that I would like to include in a post, if nothing else as a handy reference for my own memory.   They also seem to be tied in some way to concerns or questions that we should have about learning analytics.  I would also like to address some of George’s concerns in the LAK-11 forum: Critiques of Learning Analytics?
First some resources:

Monday, January 24, 2011

LAK11 : Where do we find good critiques of learning analytics?

George Siemens started the discussion in LAK-11 with both a post and a forum.  This post is in response to his question.

I’ve come to the preliminary conclusion that because the field is so new-especially referenced by the exact phrase “learning analytics” that there probably aren’t a lot of good critiques.  However, if you consider one (ugly step?) sister from standardized testing – or  google “critiques of standardized testing”  there is a plethora of critiques and opinions.  Let’s try to ensure that learning analytics does NOT follow the mis-steps of standardized (mandated) testing.

I found one recent federal government publication(abstract) or (pdf) that attempts to report about the use of Educational data at the Local Level.  It focuses a lot on “data-driven decisions” and claims to address local uses of educational data from accountability to instructional improvement. The closest the article comes to critiquing the analytics is by addressing the "barriers to use" of the data.  These

Thursday, January 20, 2011

LAK11 Week 2: The Rise of Big Data

This was great stuff, from the concrete (10 Ways Data is Changing How we Live,) to the extremely abstract (Computing a Theory of Everything)  and everything in between.  In fact, there’s something for the paranoid, the optimist,  the experimenter, the database/computing guru, the physicist or chemist, and more!

Given my propensity for fascination (i.e. distraction) , many other links were followed.  Now I’m faced with the dilemma of how to synthesize all of that into something I can post that has meaning, yet represents the pool of resources (data) traversed. Should I summarize, find patterns, correlate, or simply comment on the outliers that I either especially enjoyed, or deplored? Ah…I get it now – a microcosm of what I’m studying – but an up close experience-within-an experience of how a human handles “big data” which, in my case, is MANY orders of magnitude LESS than what a computer is capable of handling. Since I’m doing the processing, I’ll forgo the exhaustive approach for the efficient. :-) How about a list of appetizing sample quotes from the resources?  [Kind of like Costco on Saturdays .  You can go find (click-on) the whole package if you like the sample.]  Here goes, and don’t forget to properly dispose of your toothpicks on the way out.

Tuesday, January 18, 2011

A Little Perspective :-)

I have a lot to say about the conversation engendered by George's post about good critiques of learning analytics but until I can get to that, I thought this video helps put things in to perspective, as we realize that no matter how "cool", emergent, or revolutionary (or even evil) we might think something is, years from now -- (or sooner!) we'll probably be smiling at our former perceptions, as the younger generation tries to figure out just how the ancient artifacts or processes were supposed to work, anyway! PS -- french audio and subtitles. Enjoy!

Friday, January 14, 2011

LAK11 Week 1: Playing with Hunch

I’m probably just as paranoid about privacy issues as anyone else in my generation, but I found that if you do a little exploring/digging  you can actually create an account in hunch that is NOT linked to your twitter or facebook accounts.  In fact , all you really need is a valid email address, and you can skip divulging  the rest of the information they ask of you to create an account.

LAK11 Week 1: Presentation - John Fritz

(Links to the elluminate session, and the mp3 and slides of that session)

John’s presentation and emphasis is (admittedly by him) biased toward the LMS/CMS, and much of the talk centered on students’ use of Blackboard.  First let me say that my interest and focus is NOT at the CMS/LMS level, but is more at the level of the individual courses—that is, using analytics to inform and improve instructional design, assessment, and evaluation.  It is interesting to note that one of the published articles to which John referred: Mining LMS data to develop an “early warning system” for educators: A proof of concept* ( Macfadyen and Dawson in the February 2010 Issue of Computers & Education) had the following to say in the conclusion-(end of section 4.1):

LAK11 Week 1: Readings

I was already familiar with ECAR key findings about Analytics in Higher Education, but so excited about finding Tanya’s paper: Learning Analytics: The Definitions, the Processes, and the Potential.   It was exactly the type of article I had been searching for – without a lot of success.  About a year ago I had attempted to begin a literature review of the use of web-anlytics in education, and there wasna’t a lot out there--at least at the level of my focus: analytics that inform and improve instructional design,  assessments, and evaluation of web-mediated learning “events”. 
blog background graphic (CC BY 2.0) courtesy Patrick Hoesly
Original T-Shirt Graphic for LAK11 Week1: Presentation post courtesy kris krüg, modified by M.R. McEwen