Educational Datamining in Australia, part II

To be able to dive into the world of educational datamining, I had gotten an interesting dataset from a colleague in Helsinki University. It included anonymized data from their introductory programming course. I’m lucky to have many colleagues who were willing to share data from their courses. So my purpose was to look at a dataset and this way learn about educational datamining.

After visiting New South Wales, I headed Monash University in Melbourne. There I had two colleagues who were familiar with educational datamining so I had a good athmosphere to get started with working!

They had set up an office with a view for me! Obviously they thought that a Finn travelling to Australia in October is in need of light therapy. You can see that they have a grass field in the middle of the campus. Almost like we have in TUT. Well, not quite. :)

They had set up an office with a view for me! Obviously they thought that a Finn travelling to Australia in October is in need of light therapy. You can see that they have a grass field in the middle of the campus. Almost like we have in TUT. Well, not quite. ūüôā

Since I had a dataset that was already preprocessed by colleagues in Helsinki University, I thought that I’ll just write a little script to organize the data in an interesting way and then I’ll be analyzing it. Little did I know… The little script was my main task during my stay in Melbourne! In education it is typically the trends over the time that are interesting for the researcher. And I found out that scripting gets quite complicated when you want to look at how multiple different things change over time and statistically analyze the changes.

“The little script” is now in a huge need of refactoring because it is doing a lot more than I thought it would. I subclassed the Python CSV-writer to better suit my purposes and did a couple of other interesting things that I had to do because now I had the time to do them…

My colleague Judy is very proficient with statistical analysis so she taught me about factor analysis. That happens to be the part that was missing in my statistical analysis course. Very useful!

My colleague Judy is very proficient with statistical analysis so she taught me about factor analysis. That happens to be the part that was missing in my statistical analysis course. Very useful!

I really didn’t expect to do so much programming here but after all I learned a lot about everything I did. And isn’t it pretty cool to be able to do all the phases yourself starting from preprocessing the data and ending with analysis and interpretation of the content? Not sure how many education or social sciences researchers are able to do that…

Greetings to the Pervasive Computing running team! I managed to finish the Melbourne Marathon despite the difficulties of arranging the weekday for the department morning jogging this autumn. At the background of this pic you can see the Melbourne Cathedral.

Greetings to the Pervasive Computing running team! I managed to finish the Melbourne Marathon despite our difficulties of arranging the weekday for the department morning jogging this autumn. At the background of this pic you can see the Melbourne Cathedral and city center.

My colleague Matt was also running. Awesome research team!

My colleague Matt was also running. Awesome research team!

And one traditional touristphoto has to be included. This is the Twelve Apostles in the Port Campbell National Park.

And one traditional touristphoto has to be included. This is the Twelve Apostles in the Port Campbell National Park.

My next stop will be University of Adelaide, so I’m moving towards the north…

 

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2 Responses to Educational Datamining in Australia, part II

  1. Pingback: Educational Datamining in Australia, part I | TUT Pervasive Computing Blog

  2. Pingback: Educational Datamining in Australia, part III | TUT Pervasive Computing Blog

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