An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

Aisa, Sitti; Akhriana, Asmah; Ahyuna, Ahyuna; Irmayana, Andi; Irmawati, Irmawati; Aini, Nurul

doi:10.3991/ijoe.v17i09.24595

Cited by 2 publications

(1 citation statement)

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“…The discovery of related knowledge in databases continues to spread to almost all fields where large amounts of data are stored and processed [7]. Data mining is the process of exploring large amounts of data based on consistent procedures such as association rules or time sequences [8][9][10], with the purpose of establishing a systematic relationship between variables by detecting new subsets of data [11]. Thus, data mining concentrates its main purpose on extracting information about the relationship between variables from large amounts of data of different nature [12,13].…”

Section: Introductionmentioning

confidence: 99%

Contributions of Data Mining to University Education, in the Context of the Covid-19 Pandemic: A Systematic Review of the Literature

Díaz-Choque,

Chamorro-Atalaya,

Ortega-Galicio

et al. 2023

Int. J. Onl. Eng.

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During the context of COVID-19, educational processes migrated to a strictly virtual scenario, so the quantity of information grew in such a way that techniques such as data mining or machine learning contributed to generating knowledge for decision-making. In this sense, it is relevant to define the state of the art of the contributions of data mining in the university environment, and from there, to see in perspective how these could be applied in scenarios of return to the face-to-face. In this sense, a systematic review of the literature is carried out, based on scientific evidence extracted from the Taylor & Francis, ERIC and Scopus databases. A qualitative content analysis approach and the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement were used to extract the findings published in scientific articles. The results were that educational data mining was applied to a greater extent in the field of “teaching”, and it was focused on the search for patterns and predictive models to improve student performance, reduce student dropout, improve the student’s quality of life, and teacher performance. In addition, as a resource for data extraction, university learning management systems (LMS) were used to a greater extent. It is concluded that tools such as data mining should be implemented as academic management policies, achieving a prospective on indicators linked to the improvement of student learning and performance.

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Section: Introductionmentioning

confidence: 99%