Intervention Effectiveness

Workshop Description

Navigate360 has a powerful tool that compares important success outcomes for different groups of students. On Friday, July 12 at 1:00 pm, learn to use the Intervention Effectiveness Tool to understand retention, GPAs, credits completed, and other metrics for students in lists that you create. 

The Division of Student Success hosts monthly hands-on workshops and open labs for faculty and staff on how to use Navigate360 and other enterprise-level data tools to understand student trends regarding enrollment, degree progress, and academic success. July’s workshop focuses on analytics to understand your interventions.

Navigate360 access is strongly recommended for this workshop. Request access at ssar.nmsu.edu. For more information, please contact Melody Munson-McGee (mmunsonm@nmsu.edu).

How can you utilize intervention effectiveness: Is your program correlated with success?

The Intervention Effectiveness Tool in Navigate360 is a powerful tool that compares success outcomes for groups of students. Users can compare student metrics for students in lists or students who meet criteria set by filters. After you define the populations to compare and the time period for comparison, Navigate360 provides metrics such as retention, graduation, GPA, and credits completed.  

This tool is available for faculty and staff who work with student success efforts and have academic administrator access to Navigate360. Intervention Effectiveness is an option on the Analytics page.

Intervention Effectiveness can help answer questions like “Since 2018 (pre-pandemic), what has happened to graduation and retention rates for students classified as a senior (90 credits or higher)?”

Graduation, retention, and attrition rates for students classified as seniors for various years. Data was pulled from Navigate360 in June 2024.

For seniors enrolled in

N, start term

Enrolled (%)

Graduated (%)

Not Enrolled (%)

Fall 2018 to Spring 2019

3,831

87.4

7.3

5.3

Fall 2019 to Spring 2020

4,405

78.1

14.7

7.2

Fall 2020 to Spring 2021

4,463

78.3

15.3

6.4

Fall 2021 to Spring 2022

4,845

81.2

11.7

7.2

Fall 2022 to Spring 2023

5,125

78.3

15.1

6.5

Fall 2023 to Spring 2024

5,149

79.3

14.3

6.4

Pre-pandemic, a lower percent of students classified as seniors graduated in the spring (7.3%; green shading) than during the pandemic (14.7% and 15.3%; orange sharing) and recovery periods (11.7% and 15.1%; blue shading). Conversely, students classified as seniors stopped out before graduation at lower rates pre-pandemic than they did during the pandemic and recovery periods.

There are many ways to use information such as this. The example can be re-worked for students in a specific college or major. Drilling into the list of students who stopped out with 90 credits or higher can help colleges and departments understand characteristics of students who are near graduation but not enrolled.

Unlike reports from the Office of Institutional Analysis for external reporting, Navigate360 reports will vary from day to day. This is because Navigate360 uses data refreshed nightly from the Banner Student Information System. Because our students can change enrollment traits at any time, counts from Navigate360 change according to student actions.

Also note that this table is not the same as graduation and retention reporting from NMSU's Office of Institutional Analysis. Those numbers are based on an incoming class of new students, whereas these numbers are based on students who have earned 90 credits or more by the term defined in the table. Students in this category will include transfer students, internal transfers from NMSU community colleges, and students who have returned to complete a degree in addition to students who started at NMSU as first-year students.

As always with educational statistics, higher retention and graduation rates for students in a certain group does not necessarily indicate that membership in that group is the cause of their success. While correlation is useful to understand, it is not causation, which takes rigorous statistical design and evaluation to establish.

This tool is described in more detail in material from EAB