Visualization analysis of educational data statistics based on big data mining

Abstract

The visual analysis method of educational data statistics based on big data mining is studied to improve students’ academic performance. Introducing the Mahalanobis distance and covariance matrix into the Fuzzy C-Means (FCM) clustering algorithm improves the FCM clustering algorithm. Through the improvement of the FCM clustering algorithm, the education data is mined from the massive original education data. The mining results are analyzed statistically, and the statistical analysis chart of education data is drawn. By improving the force-guided layout algorithm, the mined educational data points are written into the elastic graph layout to realize the visual layout. The ECharts data visualization analysis component presents the visual layout results of education data points and the statistical analysis charts of education data. Experiments show that this method can effectively mine educational data and draw statistical analysis charts of educational data. Among them, learning analysis data occupy the highest proportion (15%), and privacy protection data occupy the lowest proportion (only 1%). The method can effectively lay out the educational data points and has a better visual effect. This method can effectively present the results of statistical analysis of educational data in visual form, in which learning analysis data is the most important.