A leading university faced challenges increasing student registration and retention rates.
It needed to see an increase in registration for degrees, degree completions and optimize course administration.
Three predictive models were developed to address the identified challenges.
These were deployed to leverage data and provide valuable predictions.
Doubled conversion of leads to degree registration rates and correct identification of lead to study.
Smart identification of better qualified students to enroll yielded higher course completion rate and lowered labor costs.
The university is a global leader in higher education offering a wide variety of learning paths and degrees. The university has proven to be a forward-thinking institution that has successfully embraced AI-driven technologies to address key challenges and achieve remarkable results.
The university sought an effective solution that would not only identify prospective students with a high chance of registration but also provide insights on student persistence and enrolment for each course before every semester. The ultimate aim was to boost student enrolment across faculties, improve student retention rates and maximize the efficiency of administration. Additionally, they aimed to reduce labor costs by automating and streamlining these processes.
Qualitest, in collaboration with the AI SVP and key stakeholders from the university, developed, tested and rolled out three predictive models were developed to address the identified challenges.
The models, namely the Leads Conversion model, the Student Retention model, and the Class Registration model, were designed to leverage data and provide valuable predictions to improve student enrolment rates, prediction of course registrants and student retention.
Leads Conversion Model
The Leads Conversion model, which won a prestigious national award in 2021, serves as an active solution that runs every semester. Its primary purpose is to prioritize potential leads for registration. By analyzing various data points, the Leads Conversion model identifies interested parties with a high chance of enrolling at the university. At the same time, the model also successfully filters out many leads who are interested but will not register. This lead prioritization operation enables the university to focus its efforts and resources on prospects who are most likely to convert, optimizing enrollment rates and streamlining the admissions process.
Student Retention Model
The Student Retention model aims to provide a forecast on student degree completion. By leveraging predictive analytics, the model generates insights and visually represents them through a dedicated operational system. This intuitive representation informs the ‘persistence unit’ about the likelihood of individual students completing their degree. It enables early intervention and targeted support to improve student retention rates and ensure a higher rate of successful degree completions. It also prevents unnecessary intervention with students with very high potential to graduate.
Class Registration Model
The Registration model focuses on predicting the number of students expected to enroll in each course before every semester. By analyzing historical data and incorporating factors such as student performance in courses, course popularity, and other factors, the model provides accurate enrolment forecasts. As the current course enrolment predictions are made ad hock and often miss the actual enrolments, this valuable information enables the university to make data-driven decisions regarding course offerings, resource allocation, and scheduling, optimizing student experiences and maximizing course fill rates.
The predictive model’s ability to effectively identify and prioritize prospective students with a high likelihood of registration has undoubtedly contributed to the institution’s growth and success.
By implementing predictive models, the university was able to:
Overall, the implementation and future development of these predictive models hold great potential to enhance the university’s competitiveness, student retention, and operational efficiency. Their innovative approach to student recruitment, retention, and resource allocation showcases their commitment to delivering a superior educational experience.
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