Rough Set Based WebCT Learning

Liang, Aileen H.; Maguire, R. Brien; Johnson, Julia

doi:10.1007/3-540-45151-x_40

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Performing reduction on a set of data is one mechanism to decrease the number of rules. Reduct provided by RS generates comprehensible rules compared to other methods [27]. Liang et.al.…”

Section: A Reduct and Rules Generationmentioning

confidence: 99%

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Generated rules for AIDS and e-learning classifier using rough set approach

Sulaiman

Rahim

Pranolo

2016

Int. J. Adv. Intell. Informatics

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The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model

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“…Performing reduction on a set of data is one mechanism to decrease the number of rules. Reduct provided by RS generates comprehensible rules compared to other methods [27]. Liang et.al.…”

Section: A Reduct and Rules Generationmentioning

confidence: 99%

“…Liang et.al. [27] used RS and Rough Set-based Inductive Learning to help instructors and students with WebCT learning. Rough Set-based Inductive Learning was used to obtain the decision rules to provide the reasons for the lack of success of students.…”

Section: A Reduct and Rules Generationmentioning

confidence: 99%

Generated rules for AIDS and e-learning classifier using rough set approach

Sulaiman

Rahim

Pranolo

2016

Int. J. Adv. Intell. Informatics

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“…The proposed method gave acceptable accuracy and high dimensionality reduction without prior searching of better feature selection. Liang et al [15] used RS and RS based inductive learning to assist students and instructors with WebCT learning. Decision rules were obtained using RS based inductive learning to give the reasons for the student failure.…”

Section: Related Workmentioning

confidence: 99%

Meaningless to meaningful Web log data for generation of Web pre-caching decision rules using Rough Set

Sulaiman

Shamsuddin

Ahmad

et al. 2012

2012 4th Conference on Data Mining and Optimization (DMO)

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Abstract-Web caching and pre-fetching are vital technologies that can increase the speed of Web loading processes. Since speed and memory are crucial aspects in enhancing the performance of mobile applications and websites, a better technique for Web loading process should be investigated. The weaknesses of the conventional Web caching policy include meaningless information and uncertainty of knowledge representation in Web logs data from the proxy cache to mobile-client. The organisation and learning task of the knowledge-processing for Web logs data require explicit representation to deal with uncertainties. This is due to the exponential growth of rules for finding a suitable knowledge representation from the proxy cache to the mobileclient. Consequently, Rough Set is chosen in this research to generate Web pre-caching decision rules to ensure the meaningless Web log data can be changed to meaningful information.

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“…In [6], Rough Set Based Distance Learning improves the state-of-the-art of Web learning by offsetting the lack of student/teacher feedback and provides both students and teachers with the insights needed to study better. To extract suitable decision rules, four steps are defined as follows: 1.…”

Section: Vprs Based Distance Learningmentioning

confidence: 99%

“…Fourthly, rough sets and Bayes factor [5], is a novel approach to understanding the concepts of the theory of rough sets in terms of the inverse probabilities derivable from data. This paper, based on the previous research works [6,7,8], on the traditional rough set and rough set based inductive learning are used to assist students and instructors, and provide an instrument for learner self assessment when taking courses delivered via the World Wide Web.…”

Section: Introductionmentioning

confidence: 99%

Improved Variable Precision Rough Set Model and its Application to Distance Learning

Zhang¹,

Wang²

2007

2007 International Conference on Computational Intelligence and Security (CIS 2007)

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Improved Variable Precision Rough Set (VPRS) is proposed to extract the significant decision rules from a Student InformationTable (SIT) in the distance learning environment. Moreover, two approaches are proposed. The first approach, VPRS based on Bayesian Confirmation Measures (BCM) is presented in order to handle totally ambiguous and enhance the precision of Rough set, and to deal with multi decision classes. The second approach, the VPRS parameters are refined, especially with multi decision classes. These concepts have been demonstrated by an example. The simulated result gives good accuracy and precise information with few computational steps.

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Rough Set Based WebCT Learning

Cited by 11 publications

References 8 publications

Generated rules for AIDS and e-learning classifier using rough set approach

Generated rules for AIDS and e-learning classifier using rough set approach

Meaningless to meaningful Web log data for generation of Web pre-caching decision rules using Rough Set

Improved Variable Precision Rough Set Model and its Application to Distance Learning

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