One of my professional societies is the Society for Learning analytics Research or solar, in fact as part of my science communication outreach. I serve as the co editor of the solar nexus learning analytics block, I included a link in the resources section at the end of this module, if you're curious to learn more about nexus. When I talk about my interest in learning analytics are going to the annual learning analytics and knowledge conference, this tends to spark several questions. If I teach and write about Biology and Science Learning, why am I interfacing and networking with a bunch of computer and data scientists? What are learning analytics anyway, maybe you're also sitting there and wondering what on earth do learning analytics have to do with Science Communication? Well, let's start with defining what learning analytics are then we'll come back to how learning analytics can improve your science communication activities. They're particularly useful if you happen to be using some form of educational technology as part of your science Communication, much like what we talked about module one. Many of the insights we have, the best ways of doing science communication comes from, we know about how humans learn in traditional classrooms. But like much earlier in this course, I encourage you to expand on that definition, learning analytics can help us understand learning, give feedback on activities. And help inform decision making in classrooms and other formal educational settings, but it can also be used in the same way in science communication. Now, as we've talked about earlier in this module, educational technologies and science education can be very sophisticated. And although games like you turn our exciting ways to involve citizens in real world science also teaching science at the same time. These games also offer rich opportunities for learning analytics, capturing. Who's playing how they're playing the beginning to tease out what that experience means for the user without a large source of money, they're also inaccessible to many of us as well. I also want to point out that although learning analytics techniques like natural language processing or machine learning can be really useful for understanding learning. And educational technologies, they can also be a bit inaccessible as well without specialized training. But that doesn't mean that learning analytics are completely unavailable to a lay person websites and social media platforms are increasingly offering baseline analytics to users. You can easily collect data on engagement shares the data part of analytics and also explore the people aspect of analytics who is engaging and what are they learning. Here's an example of recent tweet concerning the blog article we discussed in module three. These are free analytics from twitter but we can see that over 1800 people saw the tweet and of that, 47 went on to engage with it in some way. Then we can see a breakdown of the replies to the original tweet from twitter, I can see who retweeted likes and what they had to say about him. I can see that people were engaging with the tweet or other science communicators. My target audience and non friends and family who unabashedly like and re share everything and put online to be supportive, which is great. But the important piece there was that I was reaching my target audience, so basic analytics can tell us if we're reaching our target audience. And as we've talked about throughout this course, reaching that target audience and writing for that target audience is very important. It also tells us if instead we're just speaking to like minded individuals in a vacuum. We can also look broadly at interactions across social media platforms and use that data to drive what kinds of post resonate with our target audience the most. Here's an example from facebook, you can see my most engaging posts were combining personal stories, for example, talking about my great grandmother on International Women's Day. Or how my son featured as part of my ted talk for how houseplants are an example of biology everywhere, which is a big part of my book outreach. This is telling me what resonates with my followers on facebook, now, when I look at twitter, I see a different story notice on twitter that even though it's a smaller timeframe. I'm regis much larger and my best performing posts are different. The blog article I wrote on bridging disciplines was posted at the same time on both platforms that had far more engagement on twitter. We also see that the personal story of my son and my ted talk feature prominently on both. But when I look at March posts, hear the story about the plant or international Women's Day which were so popular on facebook were lost on twitter. Again, we see the nexus blog post being a top tweet plus a teaching tip and getting my Covid vaccine being my most engaging tweets from March. So again, they're different between the two platforms, if you have a website, you can look at basic analytics like who is driving visit to your website. Or people coming to your website from facebook from twitter from your email list. This again tells you about your reach which social media platforms are most successful at reaching your target audience. What can you tell about learning then that's the third part of learning analytics introduced in the video we just watched. This can be harder to tease out, but many quizzes or reflection questions associated with anything from a game to a social media post can clue you into learning. Asking a guiding question on the social media post can also be useful to using out what and how people are learning science as you present it. And also tell you about what stories resonate with people, which might be useful for building on later, such as designing future outreach or communication activities. For example, earlier in this course, you had a chance to watch my ted talk on biology everywhere. Several of the examples that I chose to include in that talk were based on high interest social media posts. This idea of social listening, which is traditionally a business marketing strategy, can also clue you into how people are learning and thinking about the science. You are trying to communicate data also helps with management, the 4th piece introduced in the video, what can the data tell us about what is and is not working as mentioned in the video. It's ultimately up to us to learn how to use learning analytics data and do so in an ethical manner, we can use, learning analytics is a valuable source of feedback on everything from the blogs. We write two games we design and then use that data to inform decisions that we make about future science communication, this comes back to what we talked about module one. The importance of data to back up what we do after all of science communication isn't communicating anything to our intended audience and why are we doing it? And showing the effectiveness of what we do is important for getting resources support and just the motivation to keep going. So yes, learning analytics means using sophisticated techniques like natural language processing of what people are talking about online. Or machine learning analysis of how people engage with the science activity but there's still baseline analytics trace data that we can easily capture. And used to inform our science communication to learn about our audiences to learn about our reach. And how to effectively use social media platforms using evidence to back up what we do and why we do, it is important. After all, one goal of science communication is arguably to help the public understand how science works and using evidence to back up what counts of science knowledge is part of that. So it only makes sense of the science communicators, we also want to back up what we do with data as well. And as we've learned here, insights to the learning analytics community can be applied to science communication as well. At both an entry and an expert level, this will help us understand various measures of effectiveness in our science communication work.