Data Mining and Visualisation of Basic Educational Resources for Quality Education
Data Mining and Visualisation of Basic Educational Resources for Quality Education
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Author : Fuseini Inusah, Yaw Marfo Missah, Ussiph Najim, Frimpong Twum
|DOI : 10.14445/22315381/IJETT-V70I12P228|
How to Cite?
Fuseini Inusah, Yaw Marfo Missah, Ussiph Najim, Frimpong Twum, "Data Mining and Visualisation of Basic Educational Resources for Quality Education," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 296-307, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P228
With an increase in educational resources for the growing population, data for Basic Education (BE) is becoming larger, requiring technical data tools to analyze and interpret. This research uses classification and clustering techniques to analyze the data from public schools in Ghana to identify the challenges. Nine (9) data mining algorithms in rapid miner studio 9.10 were used for the analysis to know the most efficient algorithm suitable for the data. These are; Generalized Linear Module (GLM), Naïve Bayes (NB), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Fast Large Margins (FLM), Gradient Boosted Tree (GBT), Random Forest (RF), and Support Vector Machines (SVM). The performance of GBT was seen as more appropriate, and this algorithm's results were presented. Excerpts from the reports are also included in the form of qualitative data. A diagrammatic representation of the interoperability among levels of education for quality education has also been presented. A proposed Neural Network model has been designed for the challenges and solutions. The conclusions draw that addressing the challenges of BE requires educational policy stability and enforcement to maximize resources and minimize the challenges in schools at all levels of education.
Challenges, Classification, Clustering, Data mining, Educational resources.
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