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Nowadays, data sets of the interactions of users and their corresponding demographic data are becoming more and more valuable for companies and academic institutions like universities
when optimizing their key performance indicators. Whether it is to develop a model to predict the optimal learning path for a student or to sell customers additional products, data sets to
train these models are in high demand. Despite the importance and need for big data sets it still has not become apparent to every decision-maker how crucial data sets like these are for the
future success of their operations.
The objective of this thesis is to demonstrate the use of a data set, gathered from the virtual learning environment of a distance learning university, by answering a selection of questions in
Learning Analytics. Therefore, a real-world data set was analyzed and the selected questions were answered by using state-of-the-art machine learning algorithms.
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Nowadays, data sets of the interactions of users and their corresponding demographic data are becoming more and more valuable for companies and academic institutions like universities when optimizing their key performance indicators. Whether it is to develop a model to predict the optimal learning path for a student or to sell customers additional products, data sets to train these models are in high demand. Despite the importance and need for big data sets it still has not become apparent to every decision-maker how crucial data sets like these are for the future success of their operations. The objective of this thesis is to demonstrate the use of a data set, gathered from the virtual learning environment of a distance learning university, by answering a selection of questions in Learning Analytics. Therefore, a real-world data set was analyzed and the selected questions were answered by using state-of-the-art machine learning algorithms.
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