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

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 habit...
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Gespeichert in:

Autor*in:	Vijayan Sugumaran [verfasserIn] S. Jafar Ali Ibrahim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	E-Learning Learning patterns Rough set theory Maximum dependency attributes (MDA) Maximal dependency

Übergeordnetes Werk:	In: International Journal of Intelligent Networks - KeAi Communications Co., Ltd., 2020, 3(2022), Seite 133-137
Übergeordnetes Werk:	volume:3 ; year:2022 ; pages:133-137

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ijin.2022.09.001

Katalog-ID:	DOAJ086026798

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callnumber-first	Q - Science
author	Vijayan Sugumaran
spellingShingle	Vijayan Sugumaran misc QA75.5-76.95 misc E-Learning misc Learning patterns misc Rough set theory misc Maximum dependency attributes (MDA) misc Maximal dependency misc Electronic computers. Computer science Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition
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topic	misc QA75.5-76.95 misc E-Learning misc Learning patterns misc Rough set theory misc Maximum dependency attributes (MDA) misc Maximal dependency misc Electronic computers. Computer science
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title	Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition
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title_full	Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition
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title_sort	rough set based on least dissimilarity normalized index for handling uncertainty during e-learners learning pattern recognition
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title_auth	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition
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Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition

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