As educators, we have access to a lot of data about our students, their learning and their engagement with learning materials and activities. How do we make sense of that data to understand how our students are progressing and how we can best support their learning? Queen Mary’s new Guide to using Learner Engagement Analytics is here to help!
What are learner engagement analytics?
Learner engagement analytics are simply pieces of data about learners’ interactions with learning activities, materials, resources and environments. There are five broad categories of learner engagement analytics you might consider, although not all of these will be relevant for you and your students:
- QMPlus data;
- Attendance data;
- Assessment (formative or summative) data;
- Contact with staff;
- Co-curricular activity data.
How can we use them to support students’ learning and improve our teaching?
Learner engagement analytics can help us get a sense of how our students are engaging with our teaching and our online learning activities and materials. We can use this information to help us identify students who may be at risk of disengagement and require support. We can also use it to reflect on, evaluate and improve our teaching (Siemens, 2013). We might also help students engage with their engagement analytics to track their learning progress and guide the development of positive learning behaviours.
The first step in using learner engagement analytics is to decide what data matters and will give you meaningful information about your students’ learning. Which activities are essential for student success in your module? Conversely, what activities or materials would cause concern if you discovered that a student was not engaging with them or struggling with them?
Once you have identified which learning activities or materials are important, think about what you need students to do with them. Is it enough that they simply complete or attend the activity, or do you need them to more actively engage or demonstrate that they can do or understand something?
Identifying these two things – which activities matter and what you actually need students to do in/with them – helps you identify the learner engagement analytics that are important for your module. Check out the new Guide to using Learner Engagement Analytics to work through this process step-by-step and develop your own Learner Engagement Analytics Plan.
Tell me more about the new Guide…
The Guide to using Learner Engagement Analytics is a self-paced, self-enrol, online course in QMplus. An upgrade and extension of the LEA Fundamentals course, the course now includes sections on:
- Learner engagement analytics and learning design
- Planning your learner engagement analytics approach
- Using learner engagement analytics in fully-online courses
- Communicating and engaging students with learner engagement analytics
- QMplus tools you can use to help students engage with learner engagement analytics.
We have highlighted links to the University’s Student Learning Engagement Policy, and taken a critical approach to learner engagement analytics, including addressing issues of equity, inclusion and well-being.
These updates and extensions were led by Jo Elliott (Reader, Learning Design, FMD) as part of her Queen Mary Academy Fellowship, with support from Usman Naeem (Reader, Computer Science Education, FSE and Queen Mary Academy’s Learning Analytics lead) and Elise Gasser (Innovation and Learning Manager, Queen Mary Academy).
You can access the Guide to using Learner Engagement Analytics on QMplus now.
References
Siemens, G. (2013). Learning Analytics:The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851