>50 Variables to Study for Student Success Impact

When I arrived at Southern Methodist University as the Director of Student Success & Retention, I was perfectly placed to join a team of student success experts maximizing student data to predict student success. There might never had been a job description more perfectly suited for me – where I have sometimes been referred as the assessment and data on student success person. Reflecting back on my time at SMU & elsewhere, I want to share data my teammates and superiors were studying in order to predict and improve student success.

Should you be interested in seeing the usage of this data in practice, I would encourage you to contact Dustin Grabsch, Assistant Vice Provost at SMU (as of 2023), who worked with me & others to gather and study this data, while he/we implemented follow-up mechanisms that:

1. maximized automation for routine tasks,
2. adopted technology that increased efficiency
3. studied process for improvement weekly, and
4. trained staff on important knowledge for effective work.  

I. Pre-Admitted Data (data that can be used (I’m not saying it should) to decide if a student should be admitted based on their likelihood of success)

Academic Achievement

1. SAT/ACT scores
2. High School Rank
3. Scores on AP tests

High School Status

• Private/Public High School
• Zip Code of High School (indicator of wealth)
• Amount and type of AP/Baccalaureate courses available

Demographic Data

1. Gender
2. Race
3. In/Out of State
4. International status

Financial Data

1. Pell Grant recipient (full & partial can be broken out)
2. Amount of Unmet Need (&/or loans taken out)
3. Household Income

Parent/Guardian Social Capital

1. Neither parent/guardian graduated college (= 1^st gen student)
2. Parent graduated from SMU
3. Parent interacted with Parent Association

Transfer Data

1. Credit hours transferred in pre-first year
2. Classified as a transfer student (& from 2- or 4- year college)
3. Grades in transferred courses

II. Pre-Matriculation Data (data that can be used for prediction before the student starts classes)

Academic Emphasis

1. Not pre-admitted to School of Business
2. Enrolled in School of Engineering
3. Pre-med emphasis with lower SAT/ACT scores

Housing

1. Live on/off campus
2. First year single or roommate
3. Specific residence hall

Notable Categories

1. Receive services from learning accommodations
2. Varsity athlete
3. Enrolled in remedial courses

III. Enrollment Data (data that can be begin to be used after a semester in college)

GPA

1. First semester & cumulative GPA
2. GPA in major courses
3. Placed on Academic Warning, Probation, or loss of merit aid

Credit Hours

1. Average credit hours/semester
2. Number of courses dropped &/or withdrawn from
3. Attempted vs completed credit hours each semester

Negative Academic Indicators

1. Recipient of early or mid-term semester alerts
2. Failed to schedule or attend academic advising appointment
3. Failed to use degree planning software
4. Submitted assignments in the Learning Management System on regular basis

Student Involvement

1. Member of student organization
2. Positional leader in student organization
3. Member of other out-of-class group (orchestra, band, theatre, etc.)

Member of Greek Organization

1. Registered for Greek recruitment but do not become a member
2. Member of Greek organization
3. Living in the house of a Greek organization

High Impact Experiences

1. Actively seeking out or doing study abroad
2. Actively seeking out or doing undergraduate research
3. Actively seeking out or doing thesis/capstone

Latent Data

1. Student card swipe frequency at the recreation center(s)
2. Student card swipe frequency at the dining halls (assuming meal plan)
3. Number of intramural or club sports students participated in

Negative Non-Academic Indicators

1. Responsible for Student Conduct violation
2. Did not pay tuition bill on time (or on payment plan)
3. Not a member of any out-of-class commitment