Education and educational institutions are going to through historical data driven technological transformation. Means, techniques and delivery methods of education have been greatly changed over last one decade. MOOCs and distance education have emerged as cheaper source of quality education. That is why colleges and universities are forced to adapt to new methods to attract best talent into their study programs. They need to upgrade and start new study programs to meet emerging business, social and scientific challenges.
Data science is already helping colleges and universities in many ways to reach out relevant talent, accepting them into programs, awarding scholarships to needy students and then delivery personalized curriculum and assessment to the students.
Universities have large amounts of historical student data including student background, courses they studies, exams, quiz and assignments they took, their performance in each course and how did they do in your professional career. From data science perspective, this is highly valuable information and can be used to build a wide range of machine learning models and advance analytics, which could then be used to take informed decisions. Some of data science and machine learning applications in higher education are given below:
Targeted Campaign of Study Programs:
There can be thousands of domestic and international colleges from where students may apply for a degree program. It is impractical for a university or college to advertise at every single school and attract best relevant talent. Here machine learning and data science can help through analytics and models built from historical student data (which college has been collecting over past). For such models, external data sources provide useful complementary information to find patterns and correlations. For colleges and universities, these models and analytics have great potential to target students who would have higher chances of success minimize cost and improve efficiency.
Predict Scholarship Award:
Not all top talent can afford university education. Likewise, not all needy talented student can be award scholarship. Therefore, colleges and universities need to have very precise method to award scholarships to those student who could be successful both academically and professional. Machine learning can help here to build prediction methods based on previous scholarship award records.
Personalized Study Programs:
Every admitted student is different. Study programs should be such that they can help students to exploit their strengths to the maximum and then cover areas of weakens by providing additional resources. Again data along with some expert supervision can be very instrumental to get this task done.
Program Validation and Assessment
Gone are those days when instructors had to manually mark quiz, assignments and examination sheets. Universities and colleges are generally employing technology toward automatic and collaborative quiz and assignment correction. Data science and machine learning help colleges and universities to take this technology based assessment to next level. We can build models which would not only do student assessment but based on student assessment they could assess programs at university levels.