How do you share your learning analytics data insights?

The quantitative data we collect must be analysed and divided into categories. Based on our approach and our goals we use the proper category. For example, planning for future lessons to enhance the abilities of students who are under the average requires sharing students' results in formative and summative assessments. Such data must be shared by the head of subjects and teachers. Whereas data about students' attendance must be shared with the supervisors, parents and teachers as well. What makes difference in my opinion is how this data is used later. Displaying data must be accurate, clear, visually appealing and quantitative.

Based on my experience, before you share learning analytics data, consider why this data is important and relevant, who your audience is, what their goals and interests are, and how they prefer to receive information. Not everyone who receives data will gleefully accept findings or the analysis of it. If you are sharing data with instructors, you could focus on pedagogical implications, best and promising practices. If you are sharing data with leaders or administrators, you might highlight the outcomes and impacts. If you are sharing data with learners, you could emphasize feedback, feedforward and guidance. You also need to tailor your language, and tone. Tone is everything, it either propels or repels the message going forward.

Knowing your audience is crucial when presenting learning analytics data, as it guides the content, tone, and structure of your message. For fellow instructors, concentrating on pedagogical implications can spur meaningful dialogue about best practices. When conversing with administrators, the focus could shift to outcomes, thereby underlining the course's efficacy and possible scalability. Learners would likely benefit most from actionable feedback and personalised guidance. Tailoring the presentation format and linguistic choices to match your audience's familiarity and interest level in analytics can significantly enhance the data's impact.

I would just show results from actual processes student actually made. I would keep copies of their progress. Keeping actual copies of the physical progress is vital to me.

One thing I've found that works for children, works just as well as for adults, and that is story telling. Stories provide context that helps your audience understand the real-world implications of the data. Weaving a narrative around your data, using anecdotes or real-life examples, tends to make the insights relatable and engaging. Numbers and data are abstract and overwhelming already. A story will provide context, making the numbers meaningful and relatable to actual situations and people. In my case, as a licensed financial consultant with 20+ years of experience in public school education, storytelling makes my presentations of financial analytics data more effective, engaging, and impactful.

Context is all about how the data you’re presenting has practical value to the problem at hand. Make sure you review and align your learning data analytics information directly relates to a challenge your stakeholders are facing. If you don’t know what that is, just start by asking what high-level problems there are at a department or institution and see how your analytics data relate in any way to that problem.

In order to get buy in on my campus's data analytics project, we're finding it's critical to make sure everyone has a few key pieces of information: 1. Why are we doing this? 2. What is the lifespan of this initiative? Education is notorious for implementing initiatives and changing horses midstream or producing info that seems to go into a void. 3. What is the expected impact on students/learning? 4. How should I expect my teaching and course design to be impacted by this initiative? Many of your faculty are likely adjuncts who aren't on campus FT. They're probably juggling teaching assignments at multiple institutions. They care deeply, but their time is limited. Make a strong case for why the project matters.

It is important to share the results of learning analytics and their interpretation using an appropriate format suitable to your audience. I also think it is important to decide the frequency of generating reports. If your goal is to improve students' attainment of learning outcomes, you may want to monitor their learning activities more frequently. Timely intervention can be applied to low-achieving students. At this point, you have to build a good rapport with your students and let them know that you are always with them in their learning journey.