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Introduction Learning analytics is the use of data to understand and improve learning. Unsupervised learning is a type of machine learning that can be used to identify patterns and relationships in data without the need for labeled data. In this case study, you will use unsupervised learning to analyze learning data from a Simulated School course. You will use dimensionality reduction to reduce the number of features in the data, and then use clustering to identify groups of students with similar learning patterns. Data The data for this case study is generated with the simulated function below. The data contains the following features: Student ID: A unique identifier for each student Feature 1: A measure of student engagement Feature 2: A measure of student performance Generating the student data with 3 features student ID, student engagement, student performance. This function takes the number of students to simulate as an input and returns a data frame with three columns: student_id, student_engagement, and student_performance. The student_engagement and student_performance features are simulated using normal distributions with mean values of 50 and 60, respectively, and standard deviations of 10 and 15, respectively.

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