In the pursuit of student success, institutions can harness the power of predictive analytics to proactively identify at-risk students and provide targeted interventions by…
Integrating Key Data in a Data Warehouse: LMS and Student Information systems provide a wealth of information (grades, attendance, participation, and demographics) which can be used to identify patterns and predict student outcomes. Using data warehousing tools to extract and then integrate this data into a single data warehouse facilitates the aggregation and analysis of this data.
Applying Predictive Analytic Methods to Integrated Data: There are several predictive analytical algorithms useful in forecasting student success and identifying at-risk students:
· Logistic Regression: is a statistical algorithm used to model the relationship between one or more independent variables and a binary outcome. In student success prediction, logistic regression can analyze prior academic performance, attendance, and socio-economic background, to determine the likelihood of a student’s success or failure.
· Decision Trees: map decisions and possible consequences, considering factors such as demographics, course grades, engagement metrics, and attendance. These trees can help identify key predictors that significantly impact student success and provide insights into the most effective interventions for different groups of students.
· Support Vector Machines (SVM) are learning algorithms used for classification tasks, and can identify patterns and create decision boundaries based on different features such as course performance, engagement, and demographics, and find the optimal separation between successful and at-risk students, enabling proactive interventions.
Viewing the Data: Analytics dashboards and visualization tools like Power BI and Tableau allow institutions to present complex predictive data visually, displaying real-time performance indicators, risk scores, and intervention recommendations, empowering stakeholders to take proactive steps.
Through the use of data warehousing tools, predictive analytic algorithms, and analytics dashboards, institutions can harness the power of data to predict and proactively address student needs more effectively.
