Learning Analytics: A journal for student engagement in higher education
Remote learning and blended learning almost overnight became the primary teaching and learning method in this academic year. With that, came the need for online learning strategies that ensure academic success. Learning analytics is the process of using student engagement data to make informed and impactful decisions to improve learning and teaching.
Every time a student interacts with their university, college, or school. So, when they go to the library, log into their LMS, or check-in in assessments and quizzes online – they create a digital footprint.
When we bring all this rich data together, we start to make sense of the metrics that matter. Leadership, faculty, advisors, and students can see a live dashboard of each student’s overall progression, retention probability, and likelihood to succeed.
In these tough times, early alert indicators can surface students with potential wellness or wellbeing issues. Then, advisors can rapidly reach out to investigate further and improve student engagement.
To help you fast-track the benefits of predictive analytics in higher education, here are our top 3 features for student engagement:
1. Real-time early alert for struggling students
A comprehensive student case management solution isn’t complete without a real-time early warning system. Institutions often waste time manually sifting through data, struggling with data silos and take the risk of slowing a student’s academic progression, or missing an at risk student entirely. Creating automated early alert workflows that can be set to trigger when a student fails to reach their required engagement criteria is crucial to student success. The key is to identify and intervene before it’s too late.
2. Track and measure higher education student engagement metrics that matter
Take a more proactive approach by regularly monitoring student engagement levels and re-engaging students before they critically drop-off. Student analytics should be clear, concise, and represent ongoing academic engagement levels. Beyond this, learning analytics need to reflect real life. Score a student’s engagement levels relative to that student’s cohort.
Student engagement dashboards can be customised and configured, meaning important engagement metrics for each cohort can be weighted and scored differently. For example, real life learning engagement for an engineering student would be based heavily on the time spent in labs; while a law student might have more weight put on their virtual learning environment activity and interactions with the library and online resources, like using Microsoft Teams for education or attending lectures on Zoom.
If a student’s engagement levels with an important part of their education begins to dwindle, your learning analytics are now in a position to help you identify students struggling with online learning and make impactful interventions early. Use effective education analytics as part of your wider student retention strategies.
3. Give students ownership of their engagement
Give students information on how they are progressing and what they need to do to meet their educational goals. Learning analytics, from the student perspective, can provide the opportunity to take control of their own learning. This gives them a better idea of their current performance in real-time and helps them to make informed decisions about their engagement.
Summing up
Learning analytics has the potential to transform the way we measure impact and outcomes in learning environments and understand student engagement. It enables providers to develop new ways of achieving excellence in teaching and learning. Moreover, it provides students with new information to make the best choice about their education. Universities should grasp this opportunity to capture and use student engagement data, so that a student is never lost in blended learning.
