What is the mining of educational data?

Educational data (EDM) is a process of data analysis obtained from schools, students and administrators. The analyzed data is obtained from computer information systems such as test scores and attendance records. Data mining is looking for patterns and associations to draw performance and behavior conclusions. Software applications are also used to manage student lessons, facilitate learning and testing process. Communication between students, teachers and parents also becomes largely dependent on the Internet and computer technology. The ability of educational data is trying to combine all this data and reveal new knowledge. This technique can be used to determine what conditions help students learn to be exams. Employment of educational data has become so popular that worldwide conferences are regularly held to teach teachers about techniques and discover new ways to integrate them into schools.

Which topics examined during educational data mining conferences include how to use data mining, how to minimize data, methods of educating software, and how to interpret data mining results to improve class teaching. Just as traders use data mining to reveal associations between habits to buy consumers and marketing activities, mining of educational data is trying to discover unspoken behavior patterns. For example, educators could use it to determine the effectiveness of experimental forms of learning and feedback for secondary school students, such as established independent learning and evaluation of subjective written reviews rather than in the letter class.

Data mining is a way to get insight into the minds of students and administrators that can be difficult to detect using direct research methods. Some universities and universities may analyze the results of students' graduation graduation on national standardized tests to monitor the quality of teaching in class. High scores in certain areas in front of others may indicate the need to adjust the method in which this material is supplied. Other teaching tools, in addition to the traditional lecture, can be tested as a result of data mining.

For example, if data mining reveals that students retain more information over time as a result of projects than on selection tests, teachers can start implementing more projects in all classes. Data mining can also isolate how a certain group of students learn. Student performance results can reflect trends between age groups and gender.