ABSTRACT
This chapter discusses research design and methodology adopted to carry out educational data mining study and analysis. It defines a step wise procedure for research framework design. Educational data mining includes various algorithms that are different in their methods and goals. As part of the data preprocessing and to get better input data for data mining some preprocessing tasks are applied for the collected data prior to loading the dataset to the data mining tool, unwanted attributes should be removed. Functions and packages in R are selected and installed for the purpose of mining. Graphical tools allow visually exploring the dataset’s characteristics to help us in understanding it. Distribution of values can be reviewed visually during the beginning of data mining project. R is one of the data visualization languages, has many options for graphically presenting data. Raw data and algorithm results can be visualized through graphics such as histograms and density diagrams.
