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Is the value of your enterprise analytics SAAS or AI product not obvious through it’s UI/UX? Got the data and ML models right...but user adoption of your dashboards and UI isn’t what you hoped it would be? While it is easier than ever to create AI and analytics solutions from a technology perspective, do you find as a founder or product leader that getting users to use and buyers to buy seems harder than it should be? If you lead an internal enterprise data team, have you heard that a ”data product” approach can help—but you’re concerned it’s all hype? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I share the stories of leaders who are leveraging product and UX design to make SAAS analytics, AI applications, and internal data products indispensable to their customers. After all, you can’t create business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release interviews with experts and impressive people I’ve met who are doing interesting work at the intersection of enterprise software product management, UX design, AI and analytics—work that you need to hear about and from whom I hope you can borrow strategies. I also occasionally record solo episodes on applying UI/UX design strategies to data products—so you and your team can unlock financial value by making your users’ and customers’ lives better. Hashtag: #ExperiencingData. JOIN MY INSIGHTS LIST FOR 1-PAGE EPISODE SUMMARIES, TRANSCRIPTS, AND FREE UX STRATEGY TIPS https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes

Tuesday Apr 07, 2020
Tuesday Apr 07, 2020
Simon Buckingham Shum is Professor of Learning Informatics at Australia’s University of Technology Sydney (UTS) and Director of the Connected Intelligence Centre (CIC)—an innovation center where students and staff can explore education data science applications. Simon holds a Ph.D from the University of York, and is known for bringing a human-centered approach to analytics and development. He also co-founded the Society for Learning Analytics Research (SoLAR), which is committed to advancing learning through ethical, educationally sound data science.
In this episode, Simon and I discuss the state of education technology (edtech), privacy, human-centered design in the context of using AI in higher ed, and the numerous technological advancements that are re-shaping the higher level education landscape.
Our conversation covered:
- How the hype cycle around big data and analytics is starting to pervade education
- The differences between using BI and analytics to streamline operations, improve retention rates, vs. the ways AI and data are used to increase learning and engagement
- Creating systems that teachers see as interesting and valuable, in order to drive user adoption and avoid friction.
- The more difficult-to-design-for, but more important skills and competencies researchers are working on to prepare students for a highly complex future workplace
- The data and privacy issues that must be factored into ethical solution designs
- Why “learning is not shopping,” meaning we the creators of the tech have to infer what goes on in the mind when studying humans, mostly by studying behavior.
- Why learning scientists and educational professionals play an important role in the edtech design process, in addition to technical workers
- How predictive modeling can be used to identify students who are struggling—and the ethical questions that such solutions raise.
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Designing for Analytics Podcast
Quotes from Today’s Episode
“We are seeing AI products coming out. Some of them are great, and are making a huge difference for learning STEM type subjects— science, tech, engineering, and medicine. But some of them are not getting the balance right.” — Simon
“The trust break-down will come, and has already come in certain situations, when students feel they’re being tracked…” — Simon, on students perceiving BI solutions as surveillance tools instead of beneficial
“Increasingly, it’s great to see so many people asking critical questions about the biases that you can get in training data, and in algorithms as well. We want to ask questions about whether people are trusting this technology. It’s all very well to talk about big data and AI, etc., but ultimately, no one’s going to use this stuff if they don’t trust it.” — Simon
“I’m always asking what’s the user experience going to be? How are we actually going to put something in front of people that they’re going to understand…” — Simon
“There are lots of success stories, and there are lots of failure stories. And that’s just what you expect when you’ve got edtech companies moving at high speed.” — Simon
“We’re dealing, on the one hand, with poor products that give the whole field a bad name, but on the other hand, there are some really great products out there that are making a tangible difference, and teachers are extremely enthusiastic about.” — Simon
“There’s good evidence now, about the impact that some of these tools can have on learning. Teachers can give some homework out, and the next morning, they can see on their dashboard which questions were the students really struggling with.” — Simon
“The area that we’re getting more and more interested in, and which educators are getting more and more interested in, are the kinds of skills and competencies you need for a very complex future workplace.” — Simon
“We obviously want the students’ voice in the design process. But that has to be balanced with all the other voices are there as well, like the educators’ voice, as well as the technologists, and the interaction designers and so forth.” — Simon on the nuance of UX considerations for students
“…you have to balance satisfying the stakeholder with actually what is needed.” — Brian
“…we’re really at the mercy of behavior. We have to try and infer, from behavior or traces, what’s going on in the mind, of the humans we are studying.” — Simon
“We might say, “Well, if we see a student writing like this, using these kinds of textual features that we can pick up using natural language processing, and they revise their draft writing in response to feedback that we’ve provided automatically, well, that looks like progress. It looks like they’re thinking more critically, or it looks like they’re reflecting more deeply on an experience they’ve had, for example, like a work placement.” — Simon
“They’re in products already, and when they’re used well, they can be effective. But they can also be sort of weapon of mass destruction if you use them badly.” — Simon, on predictive models
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