How Learning Analytics & Data-Driven Design are creating user-centred digital learning
Throughout our lives we meet many different teachers, and some of them are better than others. The teachers we remember are the ones that inspired our interest for a subject, spoke to us at our level, and gave us insights that “made the penny drop”.
The digital realm is full of checklist courses that require the user to wade through information, ticking boxes until they get the desired learning output, but how do we change this experience of online learning to one where users are really engaged with the content and are enjoying the learning process? How can we make online teaching inspire in the same way as a great teacher in a classroom?
One thing that great teachers do is relate to their students: they get to know them, their interests and their learning strengths and weaknesses.
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In 2009, Steve Woods coined the term “Digital Body Language” (DBL) in relation to how consumers’ behaviour can be tracked online. In the context of the digital classroom, Learner Analytics can help us to get to know our students, and Data-Driven Design helps us to read their Digital Body Language. Clicks, drop-offs or shares can be indications of engagement or lack of it, in same way eye rolls and sighs in the classroom can tell us we need to change up our approach!
Data-Driven Design and Learner Analytics do not give us a crystal ball to see into the future, we still can’t evaluate a course before it has taken place, but we can use data to try to get a best fit with our users and plan for better learning outcomes.
Data is available from different sources, for example your intranet, LMS and website, and it can tell us a wealth of information, such as:
- What device or browser do your audiences prefer to use?
- At what time or day of the week do they access content?
- What length of content is optimal?
- Do they prefer videos or articles?
Part of putting the user at the centre of the design process involves a change of mindset and process. We need to move from a linear model of design, delivery and post-course evaluation, to a more continuous process that pre-evaluates content by researching digital behaviour and preferences, and is in constant interaction with users so design can be continually adapted.
Niles-Hofmann identifies the three phases of Data-Driven Design and calls for a simplified process of Discover Insights, Respond, Monitor. (Niles-Hofmann, 2016)
The three phases of Data-Driven Learning Design (DDLD) are:
- leveraging different learner data to gather insights to make informed design strategies
- building content against the identified preferences
- refining and adjusting content to increase engagement
Aside from improving the learner experience and engagement, Learner Analytics and Data-Driven Design can also make our learning offering more efficient and effective. We can support skills gaps with specific content rather than making users go over material they are already confident in. Leaner content can be more cost-effective to produce and, from a user point of view, snappier, tailored content can keep learning fatigue at bay and allow them to move more quickly through skill levels.
Learner Analytics and Data-Driven Design are tools that can be used to get to know your users, respond to their needs, and can elevate your online course from run-of-the mill to truly inspiring.
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- Niles-Hofmann, L (2016) Data-Driven Learning Design: How to Decode Learner Digital Body Language. http://www.loriniles.com/ebook
- Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning Analytics in Higher Education: A review of UK and international practice Full Report. JISC. https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf
- Woods, S (2009) Digital Body Language: Deciphering Customer Intentions in an Online World.