Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition

Abstract

The determination of e-learners' learning style in an online environment has raised the potential scope of interest as its exact estimation prompts a sensational improvement in the contents of the learning framework and student performance. It requires a deep investigation of the learning habits of the learner. Grouping e-learners together provides a more quantifiable way to analyze the learner's feedback and log files to discriminate them based on their learning style. This is accomplished with the help of clustering algorithms in data mining that aids in determining their learning styles well. The target clusters are analyzed by generating functional patterns or rules using the rule induction algorithms. Most of the existing works in the literature attributed to the elucidation of learning styles fail to address the uncertainty and inconsistency in the learner's characteristics. The RST is an optimal method for analyzing the learner's behavior in this context. Thus, a Rough set based least dissimilarity normalized index (RS-LDNI) is proposed for resolving uncertainty while estimating e-learners' learning patterns. This RS-LNDI used the merits of Maximum Dependency Attributes (MDA) for categorical clustering such that the maximal dependency between attributes can be determined by splitting attributes instead of Roughness. It also adopted categorical data clustering to attain the correlation between attributes that cannot be used for learning style prediction. The experimental results of the RS-LNDI algorithm outperform the demerits of these existing clustering algorithms by utilizing the reduct and equivalence class property of rough set theory.