Statistical Models for Grades Prediction :
A common curriculum for students with different learning potentials, is biased. Bases on certain recorded parameters, the CGPA/ score of a student can be predicted before the exams, thus enabling teachers to work harder on students who are likely to score low.
How do we do it?
Forecasting Machine Learning models based on Regression & time-series algorithms perform well in quantitative predictions, by leveraging historic data and patterns within it.
Dropout Rate prediction:
Due to a wide range of reasons, many students discontinue their courses at various stages of completion. By leveraging the historic data available on dropouts, patterns can be unveiled. These patterns can be looked for in any ongoing courses to identify students who are likely to drop out of a course.
How do we do it?
We utilize the data available previously on students who have dropped out, to find patterns and similarities in them. Then a binary classification model takes in a set of predefined parameters to identify students who are likely to drop out in a given time frame. This valuable information can be leveraged by administration to focus specifically on these students to improve course completion rates.
Donor and donations Management :
Insights pertaining to donations received by educational institutions can be used to streamline the process and focus the efforts and marketing spends on areas that are most likely to reap benefits.
How do we do it?
Patterns within the nature of donors, the channels used to obtain donations etc. can be used to divide donation transactions into clusters, which could reveal the donation collection practices that are performing the best. This information directly translates to efficient strategies to attract best possible donations with minimal use of resources.
Measuring and Monitoring Instructor Performance :
With the recent developments in data analytics, it is possible to keep track of a teacher’s performance in real-time based on student feedbacks and other customized data collection methods.
How do we do it?
Key Performance Metrics (KPIs) are defined to quantify Instructor performance. Unstructured data such as student feedback can be efficiently stored and analysed using big data platforms. Furthermore, Natural Language Processing (NLP) can be performed on textual feedback to quickly understand student sentiments about a particular instructor.
