Data mining is the process of using large sets of data for patterns and trends, turning those findings into insights and predictions by proactively identifying data patterns. Although computers allow large amounts of data to be compared, people are needed to analyze and interpret this data.

The data mining process gathers a variety data points to determine strengths and weakness in groups, identify patterns and provides the ability to sort the data. This data provides insight into student learning which may be difficult to see in limited to small data sets. For example, if data mining uncovers that students retain more information with hands on projects rather than paper pencil worksheets, teachers could use that information to drive lesson planning and instruction. Data mining can highlight the way some students learn and the results can be sorted into categories (i.e. gender, ethnicity, age, classroom) to uncover any patterns.

Once data is collected, the sorting and analysis of this data is done by people. The analysis of the data is the most important component in the data mining process. In an effective analysis, the data will present patterns or trends and educational decisions for students are made as a result. This step of interpretation is eventually a transformation process between quantitative results to qualitative suggestions.

The possibilities of EDM include gathering large data sets and sorting the data. This create a challenge of storing and ensuring that you have capacity within the organization to analyze data and make good decisions to drive instruction especially at the elementary and secondary levels.

Published in Expert Syst. Appl. 2007 Educational data mining: A survey - C. Romero, Sebastián Ventura