Please share the details regarding educational data mining thesis.
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Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal / predictive studies). Because web-based education systems are capable of collecting large amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between student attributes, assessments, and Strategies adopted by students. The focus of this dissertation is threefold: 1) introduce an approach to predict student performance; 2) use clusters to build an optimal framework for grouping Web-based assessment resources; And 3) to propose a framework for the discovery of interesting association rules within a web-based educational system. Taken together and used within the online educational environment, the value of these tasks lies in improving students' performance and the effective design of online courses.
Data mining is the mine of knowledge interested in people with a large amount of data, and this knowledge is useful information, but connotative and previously unknown (Han and Kamber, 2003). Data mining techniques are acquired to satisfy application in many fields, and the application of data mining techniques at university can accelerate innovation and the development of the education system. Data mining technology can find useful knowledge of a large amount of data, and this knowledge provides an important basis for improving the decision-making process in the university management system.