Yesterday, we looked at the New Analytics within Canvas. Just because you have information available as analytics doesn’t mean everyone will use it in the same way. So today, we want you to think about the ethics of using Analytics to ensure these are being used effectively, fairly and appropriately.
Who are the analytics for?
Analytics can be defined as the practice of identifying, translating and communicating patterns in data. Simply, analytics shows us insights into data. Learning analytics focuses on data to make informed decisions to help universities increase student grades, retention and improve overall student experience.
There’s a number of people in education who will use analytics for different reasons. This includes:
- Academics / Support staff
- HE Organisations
Students should be able to manage their own learning analytics and have opportunities provided to them for timely conversations with academics whilst using real-time data analytics. This can help students better understand their learning behaviours, how much time they are spending on specific materials / activities and help students discover what else they should be spending time on within the course.
Teaching / Support staff
Through using analytics, this helps Academics / Support staff to identify at-risk students. This allows for early interventions and to enhance the students experience.
Academics can obtain a deeper understanding of students behaviour and interactions with the curriculum when viewing detailed and regular reports. Learning analytics helps deliver more personalised learning experiences by assessing learning techniques, how the VLE is used and how courses are designed.
This can be an iterative process. For example, if analytics highlight the VLE is being used heavily on a week night by the majority of students, this might be a good time to schedule an activity or discussion and engage students in their learning. It might be the analytics show not all materials are being accessed and this might be a navigation issue within the module, it could be the materials aren’t deemed relevant or the format of the materials aren’t suitable to learner needs.
Academics can get a broader view of the student cohort and how many students have viewed materials and engaged with activities. It also shows the topics learners are interested in. By comparing analytics from previous modules, academics should be able to use the information to predict future student trends and amend course design / content to maximise student learning.
Analytics can help improve student wellbeing. They can increase retention and communications between students and academics through using data-informed teaching / learning decisions. This also allows for courses to be refined with more activities included or activity times amended to suit student needs.
As an organisation, decisions must be made regards who is overall responsible for the legal, ethical and effective use of analytics in learning. Responsibilities include:
- How data is collected and used
- How data is anonymised where appropriate
- What analytic processes are to be performed data and for what purpose
Realistically, key student representatives and staff groups should be informed regards objectives, design, development, roll-out, monitoring and usage of analytics in learning. Other considerations include:
- Transparency / consent
- Positive interventions and
- Reducing adverse impacts
Tomorrow, we continue with a look at best practices in Canvas.
We hope you have a pleasant day. Please do join us then to learn more and don’t forget to follow us on Twitter: @MDBSelearn.