Ok, I’ll be the first to admit that for most of us, data is boring spreadsheets, databases, never-ending lists of seemingly random information. But as learning professionals we need to take a step back and consider not what the data means to us, but what it could mean to our students and their learning experience.
Of course, before I go into why we should care, and what type of data we should be capturing, firstly we need to make sure we have appropriate permission to capture and use our learner’s data. Different agreements can mean the difference between being able to request a student’s location for example and then using that location data to determine retention patterns based on this information or simply using location information for sending out paper-based correspondence. So, it might be worth double-checking what data agreements you have in place before you go too far down the path.
So why should we care about data?
Learner data comes in a number of types, including data about who they are as individuals (location, previous study, etc.) and current course-related data (how a learner is progressing, what materials they are (or aren’t) accessing, how much time they are interacting with different learning materials, etc.). By analysing this data, we can get a better understanding of who the learner is and what support and guidance they may need to succeed in their studies. By analysing this data for a large number of learners, across courses, locations, etc. we can use this data to get an idea of the key issues being encountered by our learners and focus our time and resources on rectifying these issues. Without this data and without appropriate analysis and planning, we may not even know these issues exist!
In many cases when developing digital learning resources, the designers/developers focus on capturing the pass/fail status of formal assessments, but often capture little else, informal assessment checkpoints, interactions with course materials, etc. are ignored. Without this additional data, all we can tell about our learners is that they attempted an assessment and they either passed or failed. As formal assessments are often one of the final elements of a learner’s journey within a particular piece of learning, we have lost a number of opportunities to identify how they are progressing through the earlier learning materials and provide guidance, support and feedback prior to the learner attempting the final assessment.
Learning resource data
Learning resource data can be invaluable in providing us with insights to how are learners are tracking. For example, e can identify which of our learning resources students are getting stuck on or not accessing, by tracking interactions with learning activities (even something as a page click can tell us something). Where a student has to complete a scored activity, which ideally should be scattered throughout the digital course materials, we then have the data we need to determine how the learner is progressing at a point in time and where required provide just in time support (eg. automatically notify a teacher if a learner scores less than 60% in a particular activity). This just-in-time support isn’t possible without data, but with it, we have the potential to improve the learner’s chance of successfully completing the course.
Demographic information, while not learning data per se, when linked with other learning data can provide us with a number of useful insights. Location data for example, when combined with data on when and how often our learners are accessing digital course materials, suddenly provides us with a number of potential insights:
- Are their patterns in learners from a particular location, that never access the course on a weeknight?
- Do learners from a particular region rarely participate in virtual classroom sessions?
By using data to determine where our learners are located and what they are doing, we can find potential issues that we can resolve to improve the learner experience. Learners from a specific location who avoid logging into their course on a weeknight, for example, may encounter poor bandwidth during these times, so avoid studying in these time periods. If a number of our learners encounter these issues regularly, we can look into alternative access arrangements where possible such as local libraries, on-campus locations, etc. Learners not participating in virtual classroom sessions may be located in a different time zone, so synchronous study does not work for them and we may need to customise our classes accordingly or provide these students with a link to a recording of the class.
These are only a couple of examples of how we can use data to understand and improve the student learning experience, the list goes on and on. Without these insights, we limit our chances of providing our students with the best chance of success. So when designing and developing your online courses, ensure you take into consideration what learner data you can capture and how you can use that data as part of your course delivery to enhance the learning experience.
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