IMPROVES STUDENT RETENTION THROUGH A DROPOUT RISK PREDICTION MODEL FOR EACH INDIVIDUAL STUDENT
It is always sad and painful to see students abandon their studies, especially if they were the first generation of their family to enter higher education.
Is a lack of detail concerning the factors that affect the dropout risk of each student a problem for your institution? Is there a feeling of disappointment that more students do not successfully complete their degrees?
risk for each student at a higher education institution at any point in time, even on the first day of class. In addition, it enables identification of those factors that are driving an increase in that risk for each student, along with their relative importance.
It is created using machine learning algorithms that review historical data for the institution and its students, such as the courses included in each study plan and students’ academic performance and dropout rate. The software processes each new piece of data added and updates its parameters, improving its predictive value.
that use historical data and predictions for the various campuses, degree programs, or other segments. Per-segment models can have greater predictive value, especially when students exhibit different behaviors in different segments.
In addition, it generates a personalized profile of each student, complete with characteristics, dropout risk, specific risk factors, and other information.
uRetention offers considerable benefits for higher education institutions:
The combined power of the features detailed above result in the most important benefit of all: uRetention provides an effective contribution to improving student retention. Availability of relevant and opportune information concerning dropout risk enables the design, implementation and monitoring of action to reduce student dropout, globally and at the individual student level.
Use of uRetention involves the following main steps:
The institution provides a set of data to be loaded into the uRetention platform:
To this are added all of the other data sets available within the institution that are of acceptable quality and which may be relevant to dropout (degree of relevance is calculated upon generation of the model in step 2):
Once the data have been loaded, uRetention generates a quality report, highlighting data that are insufficiently complete or consistent to be processed by the algorithms.
Working alongside the uPlanner team, the institution decides whether to use a single predictive model for all students or specific models for each campus, degree program, teaching format, or other segment, based on the historical data available for each.
Generally speaking, if the abundance and quality of data is sufficient, per-segment models tend to provide a better representation of the behaviors of a certain group of students and, as such, deliver more precise estimates of dropout risk.
Using the predictive model (general or per-segment), uRetention estimates the likelihood of each student abandoning their studies. Based on these values, the software generates a report identifying those students whose dropout risk is above a certain threshold predefined by the institution.
uRetention can also generate group reports, identifying those degree programs or cohorts whose average dropout risk is above a certain value.
To accompany its estimates, uRetention generates a personalized profile for each student in which it details their characteristics, their dropout risk (and evolution thereof), their specific list of associated factors (each with its relative weighting), and a log of the steps taken by the institution to mitigate the student’s dropout risk.
Institutions use the data generated by the predictive model to formulate and implement actions aimed at mitigating its students’ dropout risk. As new estimates can be generated at any point in time, uRetention is a highly effective tool with which to monitor the effectiveness of the steps taken by the institution.
More than 70 educational institutions trust u-planner solutions, ranging from small and medium institutions up to the most prestigious and highly ranked universities.