Clustering moodle data as a tool for profiling students

Bovo, Angela; Sanchez, Stéphane; Héguy, Olivier; Duthen, Yves

doi:10.1109/icelete.2013.6644359

Cited by 38 publications

(28 citation statements)

References 3 publications

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“…Traditional Data Mining algorithms have been applied to various kinds of educational systems as shown in Table I. Broadly, the educational system can be classified as two types, brick and mortar based traditional classroom's and the digital virtual classroom's better known as known as LMS Systems [19], web-based adaptive hypermedia systems [20] and intelligent tutoring systems (ITS) [21]. The application of various clustering algorithm has been applied in many a cases to educational data set in diverse studies.…”

Section: Clustering Techniquesmentioning

confidence: 99%

“…This ability of the student behavioral learning patterns has been computationally mapped by applying the Forgy method for k-means clustering and combined with Bloom's taxonomy to determine positive and negative cognitive skills set in reference to reading comprehension skills. [20]. Yet in another study, [21] combined Web Based Instruction (WBI) programs with the cognitive learning style of the learner to study their effects on student learning patterns.…”

Section: Using Clusteringin Edmmentioning

confidence: 99%

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Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

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Section: Clustering Techniquesmentioning

confidence: 99%

Section: Using Clusteringin Edmmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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“…There are also published papers about text mining that aimed at classifying educational texts [Webber, Cristofoli and Lima 2013], or classify student's messages [Bovo et al 2013, Raghavan et al 2010, clustering and monitoring discussion forums [Oliveira Júnior et al 2011, Oliveira Júnior andEsmin 2012], enhancing feedbacks in written works [Villalón, Kearney, Calvo and Reimann 2008], and helping to predict students' performance [Romero et al 2013], among others. Nevertheless, they do not provide recommendations or any other kind of help regarding students' grammatical mistakes.…”

Section: Edm and Intelligent Algorithms For Clusteringmentioning

confidence: 99%

Educational Data Mining to support Distance Learning students with difficulties in the Portuguese Grammar

Soares

Machado

Diniz

et al. 2016

Anais Do XXVII Simpósio Brasileiro De Informática Na Educação (SBIE 2016)

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“…O autor propôs ainda uma ferramenta estatística para os educadores monitorarem os alunos e seus respectivos comportamentos de aprendizagem a partir dos logs coletados do Moodle.Analogamente, Bovo et al (2013)utilizaramclusterização para encontrar diferentes grupos de alunos e se estes grupos apresentam diferenças qualitativas e quantitativas.…”

Section: Trabalhos Relacionadosunclassified