This document discusses how educational institutions can use data mining software to better understand and support their students. It outlines several areas where data analysis can provide insights, such as predicting student performance based on more than just grades, understanding factors that lead to success or failure and graduation, determining the effectiveness of support programs, identifying which recruitment strategies and financial packages attract students, and predicting those most at risk of dropping out or defaulting on loans. The overall goal is to enhance student outcomes and institutional management through analytics.

1. S
What Educational
Institutions Are
Doing and Can Do
With Data Mining
Software

2. Better predict each student’s
Performance by taking into
account More than grades
S What factors lead to student success beyond grades?
S Rethinking how to look at students to target to ensure
their success
S What factors lead to student success beyond grades?
Why are some students more successful than others?
S What factors lead toward graduation, and after how
many years?
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3. Better
manage
marketing
dollars for
recruitment.
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4. Better understanding of factors
related to struggling students,
ultimately to increase retention.
S What factors lead to failure?
S What factors lead to graduation?
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5. S
An understanding of
support programs’
effectiveness.

6. Better understanding
demographic and other factors
S Related to attracting quality students
S Related to identifying accepted students that are
unlikely to enroll
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7. Determine
which non-
need based
packages
attract the
best students.
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8. S
What factors lead to
student retention,
especially at-risk
students?
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9. S
Predict which students are
likely to default on their
student loans.
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10. S
Comment!
Which of these apply to you?
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