Clustering Educational Items from Response Data using Penalized Pearson coefficient and deep autoencoders
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Date
2022-01-24
Journal Title
Journal ISSN
Volume Title
Publisher
University of Eloued جامعة الوادي
Abstract
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.
Description
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
Citation
Harbouche, Khadidja. Smaani, Nassima • Zenbout, Imene . Clustering Educational Items from Response Data using Penalized Pearson coefficient and deep autoencoders. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]