Industry
Education
Digitalizing Education Together with Learning Technology Solutions
Solution
Data and Analytics
Harness the Power of Data and Analytics for better Business Outcomes
Highlights
- A predictive enrolment model is a statistical technique that predicts the probability of a student engaging in a desired behaviour.
- Higher education institutions must place a higher emphasis on delivering a remarkable digital experience for both students and faculty to remain competitive.
- To provide a seamless experience for students, faculty, administrators, and alumni, universities need to integrate their data and apps and automate their processes.
On this page
Big data boost
Big data influences many aspects of our personal lives and is intrinsic to business decision-making. In the latest Big Questions blog series, Kuldeep Pandita, Head - Education Business Unit, Australia and New Zealand, and Palash Agarwal, Growth Advisor, Education Business Unit, TCS, explore how universities can leverage big data and analytics to drive admissions and improve student retention rates.
The next generation of student engagement is here. For years, these ‘digital natives’ have been consuming digital information and stimuli quickly and comfortably via laptops, iPads, smartphones, and social media. Students now expect consistent, real-time access to information on housing, parking, class schedules, grades, financial aid, and more.
News reports suggest that more than half of the student respondents indicated they would consider enrolling in online courses anywhere in the world. But are education institutions across the region digitally prepared to attract, engage, and retain this new generation of ‘hybrid,’ globally minded students?
Higher education institutions face tremendous pressure to drive enrolment and retention, as they are up against a student body that is rethinking whether, when, and where they should attend university physically or go for online classes. Higher education institutions must place an even higher emphasis on delivering a remarkable digital experience for both students and faculty to remain competitive.
Most progressive universities and colleges across the region focus on providing an enhanced digital experience. They’re rising to meet the challenge by embracing the cloud, unifying their applications and systems, and putting data front and centre of their strategies to enrich the experiences of all—students, faculty, and alumni.
Unlocking the power of predictive enrolment
Technology plays a significant role in how educational institutions differentiate themselves, unlocking fresh opportunities to educate new learners via innovative business models and pursue strategic business initiatives.
Having flexible and scalable digital platforms built on trust that marries data and intelligence in a way that is student-centric and supported by a vibrant ecosystem of providers and skilled higher education users is vital.
With so much riding on the ability to deliver a seamless, connected experience for students, faculty, administrators and alumni, universities must integrate their data and apps and automate their processes. They need to connect all their disparate systems.
Colleges and universities serious about future-proofing their business model are already leveraging innovative, data-driven solutions to modernise their IT infrastructure and drive connected experiences for students, faculty, and alumni with fast, streamlined, and efficient processes.
How does a predictive enrolment model work?
A predictive enrolment model is a statistical technique used to determine the likelihood of a student performing some desired behaviour.
To do so, universities can study the behaviour of previous students to identify variables that influenced their enrolment behaviour and, assign weightage to the variables based on their relevance and calculate a “PM score or Predictive Model score” for each student. PM scores can then determine the likelihood of a student enrolling in a university.
PM scores will also help in assessing the students’ performance and determining which students will need academic support based on their talent score in the PM score.
Universities can easily identify their target segment by analysing student data over the past five years. For example, a university can understand the socioeconomic levels of students, their key capabilities, and interests. Based on this information, universities are then equipped to predict the expected enrolments for the upcoming year and thereby identify their key target segments.
The future of education is built on intelligent integration
In today’s tumultuous higher education landscape, it is more important than ever for institutions to guide strategy with data.
With competition for students at an all-time high, some established tactics for courting and retaining students are no longer viable. At the same time, shaking up an enrolment, retention, or advancement process without strong evidence could prove costly in a precarious time. Done correctly, predictive modelling can provide objective insight to inform strategic decisions across the institution.
The higher education industry faces disruption across its value chain, which means a greater need than ever to adapt. University data will not only provide precision regarding where efforts would be best placed. It can also help institutions build the case for change and measure the success of its outcomes. Now is the time to make that move.
