Integrating IRT to Clustering Student's Ability with K-Means

Chang, Wen-Chih; Chen, Shenglin; Li, Mao-Fan; Chiu, Jui-Yu

doi:10.1109/icicic.2009.247

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Various clustering methods have been used in EDM such as Mean-shift clustering, K-means, K-medoids, Densit Based Spatial Clustering of Applications with Noise ( DBSCAN ) in [21,48], and Hierarchical clustering in [49][50][51][52], however, these approaches are also not robust to identify significant clusters in ambiguous and noisy datasets [22]. A short description on these methods is as follows:…”

Section: Existing Clustering Methodsmentioning

confidence: 99%

Integration of Data Mining Clustering Approach with the Personalized E-Learning System

Kausar¹,

Xu²,

Hussain³

et al. 2018

Preprint

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Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

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Section: Existing Clustering Methodsmentioning

confidence: 99%

Integration of Data Mining Clustering Approach with the Personalized E-Learning System

Kausar¹,

Xu²,

Hussain³

et al. 2018

Preprint

View full text Add to dashboard Cite

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Hierarchical Clustering Based Teaching Reform Courses Examination Data Analysis Approach Applied in China Open University System

Liu

Ting-ting²,

Chen³

et al. 2014

2014 Seventh International Symposium on Computational Intelligence and Design

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China Open University system, as an exclusive university organization that is specialized in distance open education in China, adopts the network teaching way, and whose teaching network covered the whole country, so the system's teaching quality is increasingly attracting attention, and the teaching reform measures used to improve the teaching quality also have been taken. Apparently, utilizing data mining techniques to analyze the teaching reform course examination data is an effective method to check the effects of teaching reform measures. Clustering analysis, as an unsupervised learning, could find the rule hidden in the data completely according to the data itself. Hierarchical clustering, has the advantages of classification accurately, outliers detection easily, and doesn't need to preset the cluster number. So this paper proposes an examination data analysis approach based on hierarchical clustering algorithm to check the effects of teaching reform measures happened in China Open University system. This paper describes an implementation scheme based on hierarchical clustering, designed for teaching reform course examination data analysis, including algorithm design and application design. The effectiveness of proposed approach is verified by processing the practical examination data in China Open University system's teaching reform courses. The experimental results reveal the changing regulation of the examination data caused by teaching reform measures, and could be the objective basis for open education teaching reform.

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Integrating IRT to Clustering Student's Ability with K-Means

Cited by 2 publications

References 3 publications

Integration of Data Mining Clustering Approach with the Personalized E-Learning System

Integration of Data Mining Clustering Approach with the Personalized E-Learning System

Hierarchical Clustering Based Teaching Reform Courses Examination Data Analysis Approach Applied in China Open University System

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