Clustering Educational Items from Response Data using Penalized Pearson coefficient and deep autoencoders - Harbouche, Khadidja; Smaani, Nassima; Zenbout, Imene (University of Eloued جامعة الوادي, 2022)
Educational data mining techniques are very useful to analyze learner performance in purpose to optimize the approach of item-to-skill mapping. Therefore computing a degree of similarity between items using different measures based on the performance of the learner toward items, enhance the clustering of different items into knowledge components. This paper proposes a computational framework to group the elements of the corresponding knowledge component. The first phase of the framework represents a variation of Pearson coefficient to measure item similarity by applying a penalty score that is calculated from the number of hints taken by the learner during solving two items. The second phase applies a dimensionality reduction using deep auto encoders to improve the clustering accuracy. The experimental results show that clustering based on the penalized Pearson coefficient and the deep dimensionality reduction (PPC+DDR) outperforms basic clustering based on different similarity methods , with approximately +0.2 in Mean silhouette coefficient.
Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued
Keywords: Educational Data mining · Learner model · Machine learning · Deep learning · Item-to-skill mapping · Clustering