Revisiting interestingness of strong symmetric association rules in educational data
Agathe Merceron, Kalina Yacef · 2007
Association rules are very useful in Educational Data Mining since they extract associations between educational items and present the results in an intuitive form to the teachers. Furthermore, they require less extensive expertise in Data Mining than other methods. We have extracted association rules with data from the Logic-ITA, a web-based learning environment to practice logic formal proofs. We were interested in detecting associations of mistakes. The rules we found were symmetrical, such as X→Y and Y→X, both with a strong support and a strong confidence. Furthermore, P(X) and P(Y) are both significantly higher than P(X,Y). Such figures lead to the fact that several interestingness measures such as lift, correlation or conviction rate X and Y as independent. Does it mean that these rules are not interesting? We argue in this paper that this is not necessarily the case. We investigated other relevance measures such as Chi square, cosine and contrasting rules and found that the results were leaning towards a positive correlation between X and Y. We also argue pragmatically with our experience of using these association rules to change parts of the course and of the positive impact of these changes on students' marks. We conclude with some thoughts about the appropriateness of relevance measures for Educational data.