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Educational data mining (EDM) is a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems).
Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency.
Data on students' success in college, including whether they enrolled in remedial courses; Data on whether K-12 students are prepared to succeed in college; A system of auditing data for quality, validity, and reliability; The ability to share data from preschool through post-secondary education data systems.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
This data mining method has been explored in different fields including disease diagnosis, market basket analysis, retail industry, higher education, and financial analysis. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers.
Ryan S. Baker (born 1977 in Naperville, Illinois) is professor of education and computer science at the University of Pennsylvania, and also directs the Penn Center for Learning Analytics. He is known for his role in establishing the educational data mining scientific community, for the Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) , and ...
,: Data points where the label is known.,: Data points where the label is unknown.,: A subset of T U,i that is chosen to be labeled. Most of the current research in active learning involves the best method to choose the data points for T C,i.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]