Saturday, October 25, 2014

Data Analytics and Learning MOOC (#dalmooc) - reviewing week 1



Decided follow the Data, Analytics, and Learning MOOC from edX to further my education and deepen my knowledge of Analytics within the context of Learning.  The instructors of this MOOC have a different approach to learning such as having learners "own their learning environment" by creating artifacts and linking to other users. The Visual Syllabus looks interesting along with the extra sessions and hangouts done by the instructors.


The MOOC introduces some new tools for learners to use and share. After playing with them for a short while I felt overwhelmed given that some had some issues (that were fixed subsequently). Hopefully, over time my familiarity increases and using them becomes second nature.

One of the tools, the Bazaar Collaborative Chat tool, did not want to work for me. I tried several times to log into the system but was not paired up with any other students. 



When I was finally paired with someone, the other student disconnected within two minutes. My discussion was short and directly with virtualcarolyn to answer a series of questions. After my 5th or 6th try, I connected to a student in Dallas Tx.

ProSolo was much more interesting from a social perspective. However, the fact that I have to keep logging in (over and over) is annoying. edX remembers my credentials. Why would ProSolo not retain it for at least a period of time?

The discussion by the instructors touched on  information overload and the following quotation was shared: 
"Information overload is the elephant in the room that most neuroscientists pretend to ignore,".  "Without a way to organize the literature, we risk missing key discoveries and duplicating earlier experiments. Research maps will enable neuroscientists to quickly clarify what ground has already been covered and to fully grasp its meaning for future studies."
Alcino Silva, a professor of neurobiology at the David Geffen School of Medicine at UCLA and professor of psychiatry at the Semel Institute for Neuroscience and Human Behavior at UCLA.
That is how I felt. However, what professor Silva highlights goes much deeper. If we do not find ways to build on existing knowledge, we will be condemned to repeat more than just studies but avoidable mistakes that others have done.

The discussion then continued on the classification of analytics tools :
  • Proprietary / Open source
  • Single functionality / Integrated suites
To retain (and applicable to this class), the learning curve for learning an entire system is higher.

As for analytics, I have been experimenting with the open source R through RStudio's user interface in the past year or so. My intitation to R was with the Coursera  "Computing for Data Analysis" MOOC. The power of R is impressive and reviewing the work of talented code writers is always a joy. Recently, I have been introduced to data.table for big data analysis and would love to experiment further. 

One big bonus of this MOOC is using Tableau for visualizing data.

With the emergence of MOOCs and other online learning systems, educators have access to tremendous amounts of data on how learners are progressing through learning systems and how to intervene / help them at the appropriate point(s) to nudge them forward. One can imagine instructors doing A/B testing on different student groups and experimenting with the assignments to understand where knowledge seekers get stuck.

The key issue for me is how to fit in all what I would love to do and experiment with (including this MOOC) into an already busy schedule.