Why learning analytics are exactly what’s needed to improve university graduation rates.
GUEST COLUMN | by Viswanath Subramaniam and Sanjay Mohan
One of the most important goals for universities in North America is to significantly drive up their graduation rates by charting an intuitive and responsive course for students to get across the finish line. However, the reality across several campuses throughout the country paints a very different picture. As per a Social Market Foundation report, six percent of university students drop out after their first year, so retention is a huge concern for universities.
Several students typically enroll in a university and take up a course of their choice but a few months into their journey they are left very confused. Their academic performance takes a severe plunge and this often has a detrimental effect on the university brand while leading to huge opportunity costs as well.
Students believe better use of learning analytics could be key to tackling drop-out rates, reducing time to obtaining a degree, and helping them achieve better grades.
Is there anything universities can do to prevent such a scenario from occurring? Is there a way to track student learning behavior to understand where the students are failing and intervene to help them complete the course with the intended success? Learning analytics can go a long way in solving these problems by providing timely insights to academic decision makers. In fact, according to recent research by ITProportal, 76 percent of students believe better use of learning analytics could be key to tackling drop-out rates, reducing time to obtaining a degree, and helping them achieve better grades.
Students leave footprints along a digital path as they use a library or engage in a virtual learning environment or interact with other e-learning applications. Learning analytics is quite simply the process of tapping into this data to improve the learning and teaching experience.
It is important to clearly distinguish it from a Learning Management System (LMS), which typically only records learning events that happen within that system and a Learning Record Store (LRS) as a learning analytics platform typically contains a LRS, but adds significant reporting and analytics capabilities not typically found in it.
Traditionally, higher educational institutions have been plagued with problems like low course completion rates and finding actionable insights on student success indicators. Institutions have a lot of data from student information systems, declared data and VLE’s that can be used for learning analytics.
Based on pre-defined KPI’s, a Learning Analytics platform churns out actionable insights, which enables the decision makers to take actions resulting in much higher course completion rates for the students and overall in a higher student success rate, among other benefits. Additionally, institutions can draw a lot of customized insights pertaining to various aspects of course delivery based on relevant KPI’s.
Data from sources like the VLE, the SIS, library systems and students own declared data feed into the learning analytics warehouse. At the heart of the architecture is the learning analytics engine where predictive analytics are processed and lead to action coordinated by the alert and intervention system. Visualizations of the analytics for decision makers are available in dashboards and a student app allows students to view their own data and compare it with others.
Key beneficiaries of Learning Analytics at a University
If implemented correctly, these are the key beneficiaries of Learning Analytics within Universities:
- University administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.
- Students/learners to reflect on their achievements and patterns of behaviour in relation to others
- Instructors and support teams that plan supporting interventions with individuals and groups
- Groups such as course teams trying to enhance the current courses or develop new curriculum offerings.
Challenges in adopting Learning Analytics at Universities
While Learning Analytics looks like an indispensable tool for the Universities, there are some challenges in its deployment. If we take a close look at University data, they are mostly in silos and there is a need to first integrate these disparate systems to get the unified flow of data required for analytics. As a result, learning analytics companies with system integration capabilities are a great fit for smooth deployment of a learning analytics platform.
Bridging the learning gap between campuses to corporate houses
The application of learning analytics isn’t just restricted to universities but also has great applicability in the corporate world. As employees in most businesses are expected to rapidly skill and re-skill to meet the demands of an evolving workplace, the importance of corporate learning programs cannot be understated. Learning analytics are incredibly important as they bring in the ability to accurately predict learning behavior of employees and appropriately create customized learning plans.
In sum, learning analytics are here and they are quietly revolutionizing the learning experience in classrooms and cubicles across the world. It would be a timely and worthwhile investment for any university or corporate entity alike as it not only helps learners excel like never before, but also ensures compliance with rapidly emerging new compliance frameworks.
Viswanath Subramaniam is Director and Head of Enterprise Platforms, and Sanjay Mohan is Senior Manager, IP Led Solutions, Product Engineering Services at Happiest Minds Technologies.