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:
Designing for Analytics
simon.buckinghamshum.net
Simon on LinkedIn
#experiencingdata
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
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives
106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley
105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need
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