[liveblog] Mirka Saarela and Sanna Juutinen on analyzing education data
I’m at the STEAM ed Finland conference in Jyväskylä. Mirka Saarela and Sanna Juutinen are talking about their analysis of education data.
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NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people. |
There’s a triennial worldwide study by the OECD to assess students. Usually, people are only interested in its ranking of education by country. Finland does extremely well at this. This is surprising because Finland does not do particularly well in the factors that are taken to produce high quality educational systems. So Finnish ed has been studied extensively. PISA augments this analysis using learning analytics. (The US does at best average in the OECD ranking.)
Traditional research usually starts with the literature, develops a hypothesis, collects the data, and checks the result. PISA’s data mining approach starts with the data. “We want to find a needle in the haystack, but we don’t know what the needle looks like.” That is, they don’t know what type of pattern to look for.
Results of 2012 PISA: If you cluster all 24M students with their characteristics and attitudes without regard to their country you get clusters for Asia, developing world, Islamic, western countries. So, that maps well.
For Finland, the most salient factor seems to be its comprehensive school system that promotes equality and equity.
In 2015 for the first time there was a computerized test environment available. Most students used it. The logfile recorded how long students spent on a task and the number of activities (mouse clicks, etc.) as well as the score. They examined the Finnish log file to find student profiles, related to student’s strategies and knowledge. Their analysis found five different clusters. [I can’t read the slide from here. Sorry.] They are still studying what this tells us. (They purposefully have not yet factored in gender.)
Nov. 2017 results showed that girls did far better than boys. The test was done in a chat environment which might have been more familiar for the girls? Is the computerization of the tests affecting the results? Is the computerization of education affecting the results? More research is needed.
Q&A
Q: Does the clustering suggest interventions? E.g., “Slow down. Less clicking.”
A: [I couldn’t quite hear the answer, but I think the answer is that it needs more analysis. I think.]
Q: I work for ETS. Are the slides available?
A: Yes, but the research isn’t public yet.
Categories: ai, education, liveblog, machine learning dw
