I'm more or less settled in my new job. The major findings:
- The key that opens my office door also fits on the bicycle parking lot.
- All employees have a key for the coffee machine. Initially, I thought this was to prevent that students get free coffee, but it is intended to increase the social cohesion. This works as follows. Stick the key into the coffee machine, get coffee, walk back to the office and a few minutes later a colleague will bring back your key. This sheds an entirely new light on coffee machines as Knowledge Management tools.
- All discussions, emails and meetings are in Dutch. There is actually one non-native Dutch speaker. She is American (troetel allochtoon) and speaks some Dutch.
- The first two weeks I worked on a Windows machine waiting for a new computer on which I would install Linux. Unfortunately, the head of ICT thinks Linux is not a good idea. No one in the entire faculty is using Linux, ICT does not support Linux and given that I could also work under Windows (perhaps after following some courses!) it seemed wholly inappropriate an exception was made here.
- After a lot of discussion at the highest levels it was agreed that I was allowed to install Linux. The conditions: no calls to the ICT helpdesk and no promotion of Linux in the faculty.
The topic of my research is Educational Data Mining (EDM). For all I know, my previous research was Weblog Data Mining, Community Data Mining, Semantic Relation Extraction Data Mining and so forth. You probably get the picture: Educational Data Mining is a label that currently has little substance and scientific papers on the matter are very hard to find.
After a little internal discussion we agreed on the following. Data Mining is about extracting knowledge from data (this is a well accepted definition) and EDM is about extracting behavourial knowledge from educational data. Whereas data mining in general concentrates on large amounts of anonymous data (e.g. analysing transaction slips from shops, tracking terms used in weblogs, etc.), EDM should concentrate on finding out what learners are doing inside learning environments and understand more about their individual behaviour. The long-term aim, given that EDM is possible, is to adapt the learning environment to the learner rather than the other way around.
In order to test the approach to getting EDM research going I'm running a number of experiments on data that is available in the department. These are low-key experiments in cooperation with the researchers (Ph.D. students and post-docs) who currently perform the knowledge extraction manually. I'll post results here.
A nice thing is that I'm familiar with a lot of topic areas relevant to EDM: visualisation, the role of time, text analysis (chats and open answers in EDM). The raw data looks different, methods and techniques should largely be applicable.