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ibi 2021 Predictions: Predictive Analytics Leading Higher Education in 2021 - How to Retain Enrollment

vmblog 2021 prediction series 

Industry executives and experts share their predictions for 2021.  Read them in this 13th annual series exclusive.

Predictive Analytics Leading Higher Education in 2021 - How to Retain Enrollment

By Deepinder Uppal, VP Innovation and Technology, Public Sector, ibi

The global education system's forced immersion into remote learning in 2020 left many educators with some painful lessons. Students walked away from a rocky year with a different understanding as to the value proposition of their education while instructors, academic staff, and administrators pondered the feasibility of a sustainable enrollment model that could support pedagogy with a remote emphasis.

2021 has come forward with its own unique set of challenges, from a change in federal academic leadership, to the forced acceptance of an extended timeline before students or academic staff will reach herd immunity estimates. 2021 will inherit many of the roadblocks that ed tech has struggled with,such as deep disparities in digital access to the lack of innovative, technical solutions that can play a dual role in supporting student success and sustain  enrollment trends. Now armed with a better idea of some of the obstacles in place, 2021 has the potential to fulfill the promise of data integration and AI driven analytic initiatives to academic programs, namely supporting student and instructor engagement and institutional health in a post-pandemic economy.

With this new paradigm in pace let's explore some of the potential opportunities ed tech may have in store for us in the New Year and beyond:

AI-Personalized Curriculum 

One of the hardest lessons for educators to learn in 2020 was the fact that a one-size-fits-all model to remote learning and course content consumption was not sustainable. Ed tech has seen the value of re-charting many institutional technology roadmaps with an enhanced emphasis on AI-led course design to support an individualized approach to course consumption. 

The pairing of AI and ML models with the understanding of the cognitive, social and emotional intelligence of their students will allow educators to augment course curriculum with learning strategies. This can better  support developmental strengths and side-step comprehension roadblocks that have plagued a static, modal-consumption view of course content. With AI-guided roadmaps, individual students will be able to tailor the pace of their curriculum and self-actualize their learning objectives by consuming content that they are more prone to identifying with and executing on.

Enhanced analytics will also allow instructors to support multiple options for the consumption of course content through process driven automation. Individual courses can be configured to support a diverse number of strategies allowing students to take a lead role in how they would like to be taught parallel to outcomes-based objectives. Finally, advances in automation will also allow for near constant instructor availability supported by multiple methods for communication, such as video, forum and chatbots programmed to address commonly asked questions related to course structure, resources and instructor availability.

Predictive & Prescriptive Treatments to Enrollment Management

Even prior to the pandemic, many institutions were turning to predictive enrollment models, supported by machine learning, and prescriptive analytics to respond to emerging enrollment trends. Innovations in data science make it easier to discover patterns in financial aid allocations, student enrollment management systems, and open-source census data. Leveraging these results, universities are able to better plan their marketing campaigns to target individuals who are most likely to enroll and ensure student success upon enrollment. 

Predictive analytics will also help universities secure much-needed funding, as they struggle to hit their bottom line with thinning enrollment numbers and sporting event cancellations that are typically huge revenue drivers for many institutions. Public universities across the country rely not only on tuition funding from students, but government funding as well. Performance-based funding can be a blessing and a curse for these schools; but by using predictive analytics, schools will identify academically at-risk students and get them on track, especially as they approach their targeted graduation date. Just a handful of students can mean the difference between receiving funding and missing the mark.

Mitigating the Challenges of Remote Learning 

With online classes likely to be part of higher ed for the foreseeable future, universities should place a higher emphasis on AI-led, data driven, analytic solutions to address the challenges that come with remote learning. Interactive, integrated views will allow faculty, staff and administrators to determine how students are engaging with the material and if alternate methodologies need to be deployed. The end result: a highly interactive, repeatable, efficient learning experience that will keep students engaged and ensure success in their program of study.

By leveraging an integrated landscape, administrators will be in a position to provide well-timed interventions to ensure student success and retention even in a remote world. These analytics can determine class schedules, academic programs, and even potential career options for the student. Institutions will also use these integrated solutions to pull data from various student academic records, socio-economic statuses and financial history, extra-curricular backgrounds, and more into a single view. Having the ability to compare student success rates at large based on these different factors will allow administrators to tackle unengaged or disenfranchised student groups who may have a disparity in technology access or education.

Charting 2021 Together

With 2021 upon us, it is important for educators to realize that there are many more opportunities for ed tech than obstacles. From the integration of Learning Management Systems (LMS) to the automation of real-time feedback for student questions, 2021 will present an engaging academic environment for students. The use of data science supported by the integration of a once dispersed technological ecosystem can create a highly repeatable, extensible model to support enrollment realities and. The future of ed techech looks bright, as long as we face the future together with all stakeholders and not give into the uncertainty that often accompanies change in education.


About the Author

Deepinder Uppal, VP Innovation and Technology, Public Sector at Information Builders

Deepinder Uppal 

Deepinder Uppal is an innovator, technical problem solver, and change agent with a proven track record of defining the technical vision, communicating complex processes, and successful creation, integration, and deployment of next-generation enhanced analytics, network architecture, and all associated facets of technology related to State and Federal Government.

Published Wednesday, January 20, 2021 7:40 AM by David Marshall
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