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http://dx.doi.org/10.14352/jkaie.2022.26.5.439

A Study on Educational Data Mining for Public Data Portal through Topic Modeling Method with Latent Dirichlet Allocation  

Seungki Shin (Department of Computer Education, Seoul National University of Education)
Publication Information
Journal of The Korean Association of Information Education / v.26, no.5, 2022 , pp. 439-448 More about this Journal
Abstract
This study aims to search for education-related datasets provided by public data portals and examine what data types are constructed through classification using topic modeling methods. Regarding the data of the public data portal, 3,072 cases of file data in the education field were collected based on the classification system. Text mining analysis was performed using the LDA-based topic modeling method with stopword processing and data pre-processing for each dataset. Program information and student-supporting notifications were usually provided in the pre-classified dataset for education from the data portal. On the other hand, the characteristics of educational programs and supporting information for the disabled, parents, the elderly, and children through the perspective of lifelong education were generally indicated in the dataset collected by searching for education. The results of data analysis through this study show that providing sufficient educational information through the public data portal would be better to help the students' data science-based decision-making and problem-solving skills.
Keywords
Public Data Portal; Data Mining; Text Mining; Topic Modeling; Data Science Education;
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