Data Mining Your Enrollment Data: Enrollment Analytics 101 · Data Mining Your Enrollment Data:...
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Data Mining Your Enrollment Data: Enrollment Analytics 101 Presenter: Michael Laracy Founder and President, Rapid Insight Inc.
Transcript of Data Mining Your Enrollment Data: Enrollment Analytics 101 · Data Mining Your Enrollment Data:...
Data Mining Your Enrollment Data:
Enrollment Analytics 101
Presenter:Michael LaracyFounder and President, Rapid Insight Inc.
Information Overload!
How do we make sense of
all of our data?
Who’s inquiring?
Who’s leaving?
Who’s applying?
Who’s enrolling?
Data Mining:
The process of exploring and searching data for meaningful patterns and relationships.
Agenda:
• Some simple examples of using data mining in Higher Education
• Predictive Modeling – What is it, and how is it being applied in Higher Ed, and where is the trend going?
• Data Mining example using Rapid
Insight® Analytics
• Questions and answers
Variable mean std dev min max
HS GPA 2.83 .635 1.25 4.0
SAT Math 580 110 400 800
SAT Verbal 550 105 400 800
Distribution of Applicants by Gender
43%
57%
Males Females
35217
698
1586
2422 2457
1649
733
184 190
500
1000
1500
2000
2500
<1.01.0-1.3
1.4-1.71.8-2.1
2.2-2.52.6-2.9
3.0-3.33.4-3.7
3.7-4.0 4.0+
Distribution of Applicants by HS GPAUNIVARIATE ANALYSIS:
CROSS TABS:
What are the cross tabulations between gender and enrolled?
CROSS TABS (continued)
MEANS ANALYSIS:
Viewing and comparing the means of variables by subgroups of other variables.
MEANS ANALYSIS (with subgroups):
These simple analyses are the basic building blocks for finding statistical relationships between variables and viewing them graphically:
.
Examples of relationships found with data mining:
Examples of relationships found with data mining:
Examples of relationships found with data mining:
How do we put all of this info together to make it even more useful?
Statistical Modeling
Using history to PREDICT the future
FUTUREHISTORY
MODELS ARE MATHEMATICAL EQUATIONS
Y= α + β1X1 + β2X2 + βnXn
Y is the variable to be predicted,
X’s represent variables for each student
β’s are the coefficients
SAT Math = 430 + 70.2 * HS Freshman year GPA
Y = α + β1 * X1
SAT Math = 430 + 70.2 * HS Freshman year GPA
Y = α + β1 * X1
SAT Math = 430 + 70.2 * HS Freshman year GPA
Y = α + β1 * X1
Typically models use more than just one variable:
Y= α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7
In this example, we are predicting Y (Applicant’s probability of enrolling)
X1 = HS GPA β1= -.00008
X2 = SAT Math β2 = -.00131
X3 = Gender (male) β3 = -.0322
X4 = Distance from school (miles) β4 = -.00189
X5 = Education level (census variable) β5 = .00975
X6 = Average Income (census variable) β6 = .0311
X7 = Rural Indicator (census variable) β7 = -.0911
α = -.053
The end result is a score for each inquiry or each applicant:
Applicant ID Enrollment Probability
1822342 21.9%
1822432 7.4%
1761241 77.8%
1767771 81.8%
1766512 2.2%
1775511 88.8%
1845544 1.3%
1833355 1.9%
1775488 55.5%
1775575 12.2%
These scores can be used to rank and prioritize your inquiries or applicants:Inquiry ID Enrollment Probability
1872662 55.9%
1722262 53.7%
1233242 52.8%
… …
1586598 25.3%
1775342 25.1%
1945263 24.9%
… …
1677322 2.1%
1895117 1.9%
Have recruiter call them, send out full information package.
Email and direct to web site.
Have a student ambassador call and invite to campus tour.
Types of predictive models used in HigherEducation:
1) Inquiry-Application Models
2) Inquiry-Enrollment Models
3) Applicant-Enrollment Models
4) Retention/Attrition Models
How are these models used?
1) Save on costs
2) Make more efficient use of recruiters time
3) Implement programs to retain at-risk students
Examples of variables typically used in Applicant-Enrollment
Models:
---------------------------------------------------
HS GPA
SAT, ACT scores
Class rank
Gender
State
Distance
Major of interest
Demographics (census data)
Inquiry date, birthdate
Initial Contact Type
Question: What should the data look like?
ID HS GPA SAT Math Program State distance
54456 3.31 580 Econ MA 107
54244 2.87 550 Bus TX 1452
56545 2.28 490 Eng NJ 370
Example of data mining using data on previous years’ applicants.
Contact Info:
Mike Laracy
Rapid Insight Inc.
603-447-1980
www.rapidinsightinc.com
