This document discusses using predictive analytics in higher education to improve student academic performance. It aims to identify at-risk students early in the semester to take appropriate actions, predict department and institutional results during the semester, and introduce a new grouping mechanism to effectively coach potential students. The author recommends grouping 30-40% of students in the top group, 50% in the middle group, and 10-20% in the bottom group for additional coaching. Experimental results showed grouping strategies improved student results and enhanced the institution's ranking by overcoming challenges through targeted coaching.

1. THE ROLE OF PREDICTIVE ANALYTICS
IN HIGHER EDUCATION TO IMPROVE
THE STUDENTS’ ACADEMIC
PERFORMANCE
Dr. C.R. RENE ROBIN M.E., Ph.D
Professor (CSE) & Joint Director
Jerusalem College of Engineering
Chennai 100

2. PREDICTIVE ANALYTICS
“ The practice of
extracting information
from existing data sets
in order to determine
patterns and predict
future outcomes and
trends.”

3. OBJECTIVE
To identify the students at risk and take
appropriate decisions to improve their academic
career early in the semester.
 To predict the result of an individual,
department and institution during the semester.
To introduce a new grouping mechanism to
effectively coach the potential students

4. MOTIVATION
 The in-take of students is in two ways
Management quota
Single window counseling by TNEA
Lateral Entry
 Not necessarily, all students have equal
intelligent quotient.
 Classroom teaching alone is not sufficient

5. Percentage of students in each group –
Recommendation
GROUP PERCENTAGE
Group I 30% - 40%
Group II 50%
Group III 10% - 20%

6. Experimental Result

7. CONCLUSION
• By grouping strategy, better results are fetched
• Better results enhances the ranking of the
institution
• Overcomes personal and academic challenges
through coaching
• Helps the college managements to improve
their academic activities midway through the
course in order to improve their performance.