Educational data mining analyzes data from schools, students, and administrators to draw conclusions about performance and behavior. It is used to develop new learning programs, improve performance, and solve potential problems. Conferences are held to educate educators about the techniques and discover new ways to incorporate them into schools. Data mining can uncover insights that are difficult to uncover with direct research methods and can be used to adjust teaching methods and implement new learning tools.
Educational data mining (EDM) is the process of analyzing data obtained from schools, students and administrators. Analyzed data is obtained from computer information systems such as test scores and attendance records. Data mining looks for patterns and associations to draw conclusions about performance and behavior.
Modern educational environments rely on technology to streamline operations and keep track of important student data. Software applications are also used to manage students’ lesson plans, facilitate the learning process and administer exams. Communication between students, teachers and parents is also becoming largely dependent on the Internet and computers. Educational data mining seeks to combine all this data to discover new insights.
Schools use insights from data mining to develop new learning programs, improve performance, and solve potential problems. The technique can be used to determine which conditions help students learn better or perform better on exams. The use of educational data mining has become so popular that conferences are regularly held around the world to educate educators about the techniques and discover new ways to incorporate them into schools.
Some of the topics explored during data mining educational conferences include how to use data mining effectively, how to extract different data sources, improvement methods for educational software, and how to interpret data mining results to improve classroom instruction. . Just as marketers use data mining to discover associations between consumer buying habits and marketing activities, educational data mining seeks to discover unmentioned patterns of behavior. For example, educators could use it to determine the effectiveness of experimental forms of learning and performance feedback for high school students, such as self-directed learning and assessments based on subjective written reviews rather than notes in a letter.
Data mining is a way to gain insights into the minds of students and administrators that can be difficult to uncover with direct research methods. Some colleges and universities may analyze results of undergraduate students’ performance on standardized national tests to monitor the quality of teaching in the classroom. Higher scores in certain subject areas over others may indicate a need to adjust the method in which this material is delivered. Other learning tools besides the traditional lecture can be tried as a result of data mining.
For example, if data mining finds that students retain more information over time as a result of working on projects rather than multiple-choice tests, educators can start implementing more projects across all classes. Data mining can also isolate how certain groups of students learn. Student performance results may reflect trends across age groups and gender.
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