1. Using Predictive Analytics to Enhance Student Performance and
Reduce Student Attrition
Ronald Sarner
SUNY Polytechnic Institute
January 2018

2. The Problem “Joe Smith” - pseudonym, real student
CS major, HS Avg 82.6, SAT Verbal 700, SAT Math 630, Tier II
First semester course selections – traditional course selection methods
CS 108 – Computing Fundamentals (4)
MAT 115 – Finite Mathematics (4)
ENG 101 – Freshman Comp (4)
HIS 102 – American History (4)
FYS 100 – Freshman Experience (1)

3. Poor Joe
Joe has been set up to fail and possibly drop out – three “at risk” courses
Risk analysis – probability of getting less than a “C” for students with the same academic profile as Joe.
CS %
MAT 115 – 12%
ENG 101 – 38%
HIS %

4. Smiling Joe
Course selection using predictive analytics – no “at-risk” courses
CS 100 – Computing Seminar (4) – 2%
MAT 115 – Finite Math (4) – 12%
HIS 101 – American Hist I (4) – 22%
BIO 105 – Ecology (4) – 26%
FYS 100 – Freshman Experience (1)

5. Who Attrits?
Largest group of attrits are students in academic difficulty (GPA < 2.00)

6. Proposition
Attrition can be reduced by placing students into classes where there is empirical evidence that they are more likely to succeed.
It is particularly important that students are not taking multiple “at-risk” courses in the same semester – particularly in the first semester of the freshman year.

7. Data Collection (cont'd)
Data Set II – Demographic Data
One record per student
Student ID First semester enrolled
Major Gender
HS Avg SAT scores (Verbal/Quant)
Athlete? EOP?
Resident/Commuter Admissions Tier
Financial aid? Scholarship amount
Entered as freshman or transfer

8. Data Preparation
Data sets joined so that grade records included demographic data.

9. Course Groupings
Courses taken by students in the first semester of their freshman year – minimum cumulative enrollment of 100 cases and offered Fall 2017
BIO 215 CET 101 CHE 110 CHE 130
CS CS 108 ENG 101 ESC 110
HIS 101 HIS 102 IDS 103 MAT 110
MAT 115 MAT 120 MAT 151 MAT 152
MTC 101 NCS 181 PHY 101 PHY 201
PSY 100 SOC 100

10. Course Groupings
Courses taken by students who entered as freshmen, minimum cumulative enrollment of 100 over five years regardless of what semester taken, not in prior group, and offered Fall 2017.
ACC 201 AST 222 BIO 105 BUS 101
BUS 105 CET 102 COM 112 CTC 162
ECO 110 IDS 102 MAT 112 MAT 121
MAT 122 MAT 230 MAT 253 PHY 102
PHY 202 SPA 100 SPA 200

11. Model Development SPSS Decision Trees used
Grade recoded to S/U (S >= 'C' course grade, 'U' otherwise).
Recoded grade is the dependent variable.

12. Model Development Model developed for all Group I courses
based on first-semester freshman enrollment
Model developed for Group II courses
Based on students who entered as freshmen regardless of when course was taken.

13. Sample Model – ENG 101

14. Freshmen CS Majors Pre-registered avoiding at-risk courses (risk>30%)

15. Individual Risk Analysis Given to all incoming CS freshmen at orientation for guidance in making schedule changes

16. Short-term Future
Assess whether freshmen so advised have a reduced rate of poor academic performance.

17. Recommendations Expand to all incoming freshmen – Fall 2018
Expand to all students – requires generating other models based on both freshmen and transfer students.
Use decision rules against actual current course enrollment to identify at-risk students in the first week of the semester; engage Learning Center early.

18. Recommendations
Develop stand-alone apps (web-based, Android, IOS) for students to ascertain risk levels.
Integrate models into Banner so that students are provided with risk data at the time of registration.
Investigate further to ascertain if models can be developed that are as effective (or better) than placement tests in math and writing.