One interpretation of Eucational Data Mining is to perform data mining on educational data. This would potentially be beneficial to some of the stakeholders, in particular those who research learning environments or those scoring learner performance. A more forward looking idea is whether EDM could also benefit learners while they are learning. The key here seems to provide learners with feedback about their behaviour. Generally, in controlled learning environments, feedback is given in the form of pre-programmed prompts: "Your answer was correct". Prompts like this suggest the learner is being controlled by the learning environment and one wonders whether it is possible for learners to become part of the learning environment. An idea is to make the learner part of the environment, literally. The animation below provides an example.
The animation shows two learners chatting while solving physics problems. The learners both look at the same screen on separate computers, and a chat tool is used to communicate. To solve the physics problems the learners can, roughly speaking, chat about:
- Domain: Their interpretation of what is happening in the simulations shown on the computer screen (learning about the momentum of bouncing balls in this case).
- Regulatory: Discussing what is the correct answer, whether to move on to the next question and so forth.
- Social: Compliments (both positive and negative) and other social talk.
- Technical: What buttons to press, interpretation of the simulations etc.
In the animation the domain is the head, the regulatory chats are the body, the arms are the social talk and the legs are technical. At the end of the animation the learner on the left has a large body and a small head. Suggesting s/he was more into discussing regulatory things and little interested in discussing the meat of the domain. The learner on the right found a better balance between discussing the domain and regulatory chats, an indication of being more focussed.
Although the animation is crude, it provides pointers to what EDM could deliver in the future: the behaviour of a learner is made part of the learning environment.
The animation is based on a model of data from experiments. For each of the classifications above (domain, regulatory, etc.) typical terms and linguistic patterns are defined. For example, phrases like "I agree", "What do you think?", "The answer is 5" are regulatory, whereas "the momentum increases", "what is the value of p" are domain related.