A Q&A with Learning Analytics Manager, Marcel Lavrencic
Learning analytics is a complex area, and it can be daunting to even think about where to start. This is why we have a team dedicated to learning analytics. As part of the Learning Enhancement and Innovation Unit, the Learning Analytics Team offer assistance to staff within the University looking for data insights in order to enhance learning and teaching practices.
The Learning Analytics team is led by Learning Analytics Manager, Marcel Lavrencic, a data enthusiast with more than 25 years’ experience in education.
We chatted to Marcel about what learning analytics is, how it can be used, and how to embrace it without becoming overwhelmed. Here is what Marcel had to say:
Q. What is learning analytics and why is it useful?
Depending on who you ask for their definition of ‘learning analytics’, you’ll more than likely get a different response. I find that the best place to start is with ‘learning analytics starts with learning’. The learning process produces data, both qualitative and quantitative. If you then try to map student learning behaviour based on course design, learning activities, feedback (both for the teacher and the student), you’ll start to get an idea of learning analytics and also a realisation of its complexity. That’s one of the reasons it has different meanings to different people.
Generally learning analytics requires the context of many factors, for example program/course design, teaching approaches or student motivation, just to name a few, so the data we provide only goes part of the way towards informing decisions. There are situations where the answer is not there, but we can always work towards better understanding the problem and adapting to collect the right data to answer questions.
As with climate change, there are sceptics who have the right to believe that learning analytics is just data that doesn’t tell you anything that you don’t already know. The trick is understanding these data sets and then making incremental changes to curriculum and teaching that will impact the outcome of individual students. These data sets should make you think about how to design curriculum, assessment and teaching activities as well as how to evaluate the effectiveness of these designs.
Q. How do you take a data informed approach when it comes to course design?
There are proven tools and analytical models that can be used to inform course design. These include feedback systems for both staff and students, predictive models or simply course specific reports. If you take it back to basics, think about what data can help you answer the following questions:
- What can assist me in developing an understanding of how best to teach the types of students likely to be enrolled in my courses?
A course profile report can help contextualise student cohorts that may require specific teaching approaches or support.
- What data can assist in planning alterations to teaching and student support approaches as the semester progresses?
You should be thinking that student feedback, assessment results and analytic data displaying student activities in the LMS would be helpful.
- What are my students understanding? Are they having problems in specific areas?
You’ll probably be able to provide insights into these questions using student LMS activity data, consolidated with student assessment data and historical course data.
All these questions directly relate to course design and should be considered either before, during or after teaching the semester.
Q. For those only just delving into learning analytics, what would you recommend as an initial approach in order to not become overwhelmed?
There are many ways to wet your toes. I find it interesting that staff are often reluctant to engage with learning analytics but more often than not, they will have their own processes (sometimes familiar but time consuming) to make data informed decisions associated with course design and teaching. What learning analytics can offer is to provide the verified and validated data though models that can save a lot of time. If the model you need doesn’t exist, the Learning Analytics Team might be able to build it for you.
There are already resources available to staff though MyUni and the Learning Analytics web pages. We’re also currently working on building some contextualised case studies to highlight successful use of analytics in courses and programs. I expect that these will provide better insights into how data can be used to enhance teaching and learning practices and outcomes for students.
A less effort approach may be to attend one of the Learning Analytics Community of Practice meetings. This is an open forum for people to share ideas, good practice and resources as well as having an Impact on the institution approaches to the use of data. Contact firstname.lastname@example.org if you’re interested.
Q. What is a common misconception about learning analytics?
“You have to be a data scientist to use learning analytics.”
No, this is not the case. The assumed knowledge and research experience you have to be employed by the University is more than sufficient to actively use learning analytics. Although, to use it effectively requires that staff have an understanding of effective curriculum, teaching approaches and strategies. Obtaining data without having this knowledge will not deliver learning analytics insights.
Have you got a learning analytics question for Marcel and his team?
The Learning Analytics team can assist with:
- Data and visualisations relating to programs, courses, assessments and students
- Building data prep workflows for automation of reports
- Providing insights on how students learn
- Information and support on how to use data to improve courses and programs
- Generating ad-hoc reports including PeopleSoft, Cognos BI, MyUni etc.
- Exploring learning theory, tool and applications and provide advice on best practice
To find out more about how the team can help you, email: email@example.com