Using machine learning to predict student outcomes
A machine learning approach for tracking performances
Student retention remains one of the greatest concerns for higher education institutions worldwide. Retention rates are used as an indication of how many undergraduate students like B Tech who start their studies at a college will continue their studies at that college.
There are many factors that play a role in early withdrawal from Top Engineering Colleges in Rajasthan. Most of them are related to financial and personal reasons. Universities and colleges also lose students when they decide to study at other schools, or take online courses at other schools or decide to take their second year off.
How technologies improve performances?
Technology in the form of machine learning is being used by B Tech Colleges to improve their student support and retention rates. The value of machine learning for the improvement of retention rates lies in its predictive power. Machine learning algorithms can easily analyze vast data sets and identify students who are at risk of abandoning their studies.
For instance, it is a well-known fact that students who do poorly academically are less likely to graduate. It is also possible that poor grades in a specific class can be a precursor to a student dropping out of that particular class as well as the entire course. On the other hand, Top Engineering Colleges learned that nursing students who did poorly in Introductory Math did not succeed in completing their course. Only 10% of the students who got a C or lower in their math went on to graduate. Of the students who achieved B+ or better, 80% graduated.
Use of machine learning by institutions
Engineering Colleges in Jaipur makes use of machine learning to hold data that goes beyond course grades and demographics. Most of the countries are taking the matter a step further by accessing location and transactional data from student swipe cards. However, this will provide a perspective on the wider context of students’ lives. The thinking is that machine learning will help identify which factors relate to student retention.
There’s another reason for broadening the range of information gathered on students beyond the information on their grades. The fact is that freshmen of Top 10 Engineering Colleges who abandon their studies do so for reasons that may have nothing to do with grades. As they usually decide to leave their college in the first twelve weeks before they have received their grades.
The sooner the university finds out what these reasons are, the sooner it can implement intervention measures.
How to improve your academic performance?
Top 5 Engineering Colleges has integrated IBM Watson Analytics to improve students’ academic achievement and development. They will be able to gain insights from many student data points to help the university retain a higher percentage of students. However, it will help them to make the unexpected discovery that student’ experiences on campus accurately.
Best Engineering Colleges used this information to design various programs. They provide services that support students to become independent scholars and achieve academic success. In addition, insights from machine learning are helping tertiary institutions to attain higher retention rates. In this process, it will help students to be more successful in their studies.