Dipping my toes in the sea of 'Learning Analytics'

This morning I received data from our second run of Begin Programming: build your first mobile game. The data from previous run of the course was already with me but I had not been able to dig into them. So today I took my first steps into using RapidMiner for data analysis (using the knowledge I gathered from the FutureLearn Academic Network Workshop) and managed to run a process for sentiment analysis on user reflections.

The process showed me that out of the 300 comments I analysed 248 were positive and 25 were negative, which can be interpreted to say the majority of comments on course reflections were positive.

Image by: DigitalRalph https://farm8.staticflickr.com/7422/12938324815_a21c70e832_b_d.jpg

However, as a researcher when analysing qualitative data, I do love to 'see' all of my data. I want to read the comments and get the minute details that may be 'lost' otherwise. But when the amount of data to be analysed is huge, for example data from a MOOC with many thousands of participants, these software tools provide an invaluable help. 

My colleagues and I are doing a comparative analysis of our two runs of the Begin Programming MOOC. Now that I have taken my first steps into using RapidMiner may be I could use it as a tool to support us in our exploration. Now it is time for a hot chocolate....!