Learning analytics and data-driven decision making can help enhance the quality of secondary education by providing insights into student
performance and identifying areas for improvement. Schools can use data analytics to track student progress, identify at-risk students, and provide
targeted interventions.

Learning analytics is a field that deals with the study of the learning behaviour of students. It is a branch of data analytics that can help
educators collect data on their learners, looking for patterns in academic performance both formally and informally to enable the next steps in
learning and support for their students. Machine learning can be used to address the problem of optimizing student performance by analyzing
large amounts of data and identifying patterns that can help predict which factors are most important for student success. Machine learning
algorithms can be trained on historical data to identify which factors are most predictive of student performance and use this information to make
predictions about future student performance.