From virtual lessons to online course selection, higher education institutions use a variety of technological tools for everyday academic practices. The increased usage of these tools gives colleges and universities an abundance of data from multiple sources such as grades, attendance records, tuition fees, engagement in online systems and much more. However, research in North America shows that only 42% of academic institutions use student data effectively, while only 31% use that data to foster academic success. As higher education deals with a changing student demographic, shifts in enrolment and new, non-traditional learning journeys, data and analytics have the potential to help predict and mitigate any major changes.
In this blog, we look at the main analytics that universities should be tracking and how, when visualised properly, they can inform insights into decision-making.
What Are Learning and Education Analytics?
Universities have access to multiple data sources, such as student information systems, which help educational institutions digitise and manage student information more efficiently, or learning management systems (LMS), which host course and student-specific data.
According to Ben Daniel’s 2015 article, published in the British Journal of Educational Technology, this information can be characterised into two groups: academic analytics and learning analytics. Academic analytics encapsulate all the activities in higher education affecting administration, research, resource allocation and management. Learning analytics, on the other hand, look at metrics associated with student progress and learning. However, these analytics are worth very little unless the data is visualised in order to turn it into critical, actionable insights. So, how can you, and your institution, use this data to inform learning and teaching practices?
4 Ways You Can Effectively Use Data and Analytics in Higher Education
Higher education institutions that are effectively collecting academic and learner data are greatly benefiting from empowering their instructors, administrators, student affairs and leaders to make real-time decisions. Below, we look at the four ways universities are using this information to make smarter decisions.
1. Understanding Enrolment and Retention
Boosting retention rates is critical to ensuring an institution’s financial health, student satisfaction and long-term perception of its value. Universities can use analytics to help meet enrolment and retention goals by leveraging predictive analytics. Predictive analytics use a combination of data points, trends and patterns to predict a student’s academic progression. This information helps universities make sure that those enrolled have a high chance of staying at the institution and completing their higher education journey.
2. Measuring Student Success and Achievement
Information from an LMS can be used to track a variety of quantitative outcome metrics, such as course progress and completion, grades, engagement, and learning outcome achievement to drive academic performance. This information can help universities make informed decisions about learning efficiencies and the effectiveness of content, learning resources and assessments. With data and analytics, higher education institutions can move beyond a one-size-fits-all solution and instead offer personalised interference for students who are not hitting learning outcomes.
3. Identifying At-Risk Students and Providing Interventions
Depending on the academic institution, an at-risk student can be defined in different ways. On one hand, “at risk” can refer to students encountering academic challenges at a course level; on the other, it can refer to students who are more likely to withdraw from the university before they graduate. Data from day-to-day interactions with an LMS such as student performance, engagement and course access (combined with prescriptive analytics from other data sources) can help academic institutions identify and help students at risk.
Prescriptive analytics take predictive analytics a step further and can help predict the impact of future decisions and provide advice on the best course of action. This information helps academic institutions understand and address the challenges surrounding at-risk students and work to put the appropriate action plans in place.
4. Allocating Resources to Meet Student and Faculty Needs
By looking at data, universities can get a better understanding of the demand for specific courses and programs and provide additional resources when necessary. For example, if the course Anthropology 110 has a long waiting list, adding another section could help satisfy student needs and provide a quicker path to graduation. If it has steady enrolment, the institution could allocate additional resources and provide a teaching assistant to help improve the learning experience for students. This allows higher education institutions to meet the growing and changing needs of all academic stakeholders.
Learn More About Data and Analytics with D2L Brightspace™
Advancements in technology present an opportunity for academic institutions to measure and improve education. We are now seeing universities using technology for more than diagnostic problems and instead harnessing data for actionable insights to address issues such as enrollment, retention, engagement and more. Learn how you can use data to strategically respond to evolving learner needs and expectations.
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