From my perspective as a Learning Technologist, my primary focus for data driven decisions is focused on maintaining, supporting, upgrading, and exploring digital learning tools. This perspective is highlighted by Sclater et al. (2016) explaining the inclusion of support staff benefits in ensuring technical and organizational systems to create a cohesive data strategy. Of course the end goal of learning analytics is to support better learning and teaching online, but the related administration, support and training should be purposefully included.
One example of data currently used in my role as Learning Technologist is analytics of course feedback and service requests. With this information compiled into reports and graphs, it’s helpful in identifying reoccurring issues to upgrade, fix or redesign for further iterations. This process is great as we receive confirmation through the reduction of that specific issue being flagged, but lacks any means of catching issues ahead of time. This sparked the question, is there is further ability to introduce predictive models and interventions for issues indirectly related to the teaching and learning (Sclater et al., 2016)? Most education is focused on outcome data, but expanding the scope to technical aspects may prove helpful in driving design and helping all users have a better learning experience (Marsh et al., 2006). My team currently sets aside dedicated time to explore new methods, technologies and software, but lacks the data to provide a more specific direction. This created a personal goal to dedicate some specific time for data related study as recommended by Marsh (2006).
After reviewing current policies and considering current use of data in my context, there are definitely opportunities to leverage data and expand on a clear policies and strategy of data use beyond technical logging and privacy requirements. Data currently available in our learning management system, Moodle, has enough data to test ideas such as student engagement checks for faculty or missed content alerts for students. Before implementing more complex data driven solutions, there should be a more defined policy and directive of learning analytics that is not currently addressed. The learning analytics policy from Open University (n.d.) highlights important considerations in the consent, use and purpose of student data. Management and experts can and should collaborate to create a data strategy aligned with organization values and policies helps enable clear principals and direction. Zettelmeyer (2015) claimed it bluntly, not knowing analytics or data strategy is “no longer acceptable”.
References:
Marsh, J., Pane, J., & Hamilton, L., (2006). Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. Santa Monica, CA: RAND Corporation.
Open University. (n.d.). Ethical use of student data for learning analytics policy.
Sclater, N., Peasgood, A, & Mullan, J. (2016). Learning analytics in higher education: A review of UK and international practice. Jisc.
Zettelmeyer, F. (2015). A leader’s guide to data analytics. KelloggInsight.
Hi Zac,
You make an excellent point about needing to consider guidelines and policies around the uses of analytics. I know at TRU we have been working on developing these guidelines for quite a few years, and I think until we are sure how we want students to be able to access and use their own data, it will be challenging to get consensus on how we move forward. As an ID I would love to access more data to determine how students are using certain materials (ie videos), and then to follow up with them to gain a larger picture. Analytics are only a small part of the picture, but I think they could help us make decisions – if used carefully, and with the knowledge that they provide just one part of the story.
Michelle