The Learning Science Podcast (private feed for brendan.strong@gmail.com)

In Conversation With... Ashton Cousineau

Hey folks!One of the things I get asked a lot is: how are instructional designers & L&D professionals more broadly using AI on the ground? This week, I recorded the first of what I hope will be many 1:1 interviews with folks who are doing great work in the AI & instructional design space. My goal is to cut through the noise and help share what is happening (and not happening!) in the world of AI and L&D. In this first episode, I speak with Ashton Cousineau, Director & Performance Solutions Lead on the Global Team at KPMG. In this conversation, Ashton discusses the integration of AI in Learning and Development (L&D), emphasising the importance of understanding pain points and opportunities for using AI tools effectively. Ashton highlights the need for a strategic approach to AI adoption, focusing on enhancing workflows, improving learning experiences, and fostering collaboration among team members. The discussion also touches on the potential of AI to enable learning professionals to become more knowledgeable and effective in their roles, while also addressing the importance of prioritising people and their needs in the process of adopting new technologies.Tune in to learn more about:* How to get started with AI in L&D.* Strategic approaches to AI tool adoption, including the importance of taking a "Product Manager” mindset. * How AI can enhance L&D professionals' capabilities.* Practical advice for L&D teams feeling behind on AI adoption, with emphasis on starting small and focusing on people-first solutions* How best to test and measure the impact of AI in L&D effectiveness and quality improvementsHappy innovating!Phil 👋PS: If you’re an L&D professional who’s using AI and would like to share your experience with the broader community, drop me a message on LinkedIn! PPS: If you want to start to experiment with AI supported by me and a cohort of educators and instructional designers like you, check out my AI Learning Design Bootcamp. Get full access to Dr Phil's Newsletter, Powered by DOMS™️ AI at drphilippahardman.substack.com/subscribe

Learning Science 101

Transcript: Hey folks! Welcome to the latest episode of my Learning Science podcast - it’s really great to have you here.For the new subscribers who have joined us since last time - welcome! I’m Phil and I’ve been researching & experimenting with online & hybrid course design for over 20 years now, as an academic as an instructional designer and, more recently, as a Chief Learning Officer & founder in the world of ed tech. Today, I’m going to be talking about the question I get asked more than any other: in a nutshell, what does the research tell us about how we should design courses? What are the five big things do I need to know & do to bake the science of learning into how I design my courses?Here’s my answer, broken down into five golden rules (distilled from over 200 pieces of research on the subject): Golden Rule #1: Learners learn better when information is minimised & presented in a way that doesn’t unnecessarily tax their working memory.What it means in practice: use the Minimal Viable Content Framework (aka the 10/80/10 rule) to ensure the correct balance of content and activity. When selecting modes, be mindful of cognitive load - e.g. a 3 min video or piece of audio is far less of a strain on working memory than the same 500 words delivered as text on a page. If you read one thing…. Hardman, 2022 Golden Rule #2: Learners learn better when learning objectives & outcomes are aligned to their personal as well as professional and “in course” goals. What it means in practice: take time to understand your learners’ personal & professional pain & aspirations - i.e. you should conduct thorough discovery work to ensure that you have a crystal clear understanding of who your learner is and how to motivate them through both the content & the structure of your course. If you read one thing…. Mager, 1997Golden Rule #3: Learners learn better when the learning experience progresses from simple to complex at an appropriate level for the learners in question.What it means in practice: take time to understand your learners’ Zone of Proximal Development (ZPD). Again, this is a process of Discovery to identify what your learners can do without help and what they could never do, even with help. Between those two extremes is their so-called ZPD - the things that they can hope to accomplish with help from an expert or a more skilled peer. This is your course “sweet spot” and where your experience needs to sit in order to optimise for both motivation & mastery.If you read one thing…. Vygotsky, 1978Golden Rule #4: Learners learn better when they are driven more by internal reasons than external rewards or consequences. What it means in practice: take time to understand & articulate how the experience is connected with your learners’ broader life goals (see Golden Rule #1). In the process, to really drive their intrinsic motivation you need to make sure that your learners feel a sense of of competence & progression towards both their course & life goals (e.g. through feedback) regularly, ideally at least once per hour.If you read one thing…. Ryan & Deci, 2000Golden Rule #5: Learners learn best when the experience is as authentic as possible. What it means in practice: as a rule, the most effective design strategy is to select content, activities, assessments & modes which best replicate real scenarios. We often think of immersive learning as complex experiences involving virtual realities or work placements, but simple strategies like using case studies & having learners work on real problems using real assets (e.g. give feedback on a real life project plan) is as effective at building an authentic experience and supporting learning transfer as more complex approaches. If you read one thing…. Herrington & Herrington, 2007That’s all for now! If you have comments or questions, I’d love to hear from you in the comments section below the post. If you enjoyed this, please subscribe for more & share it with your people. Finally, if you want to get hands-on and design a course with me, you can apply to join my course design accelerator, a hands-on adventure where we work together to design a course of your choice using the DOMS™️ evidence-based principles & process.The next cohort kicks off in September and can learn more by following the link at the bottom of this post. Happy designing! 👋 Get full access to Dr Phil's Newsletter, Powered by DOMS™️ AI at drphilippahardman.substack.com/subscribe

The Minimal Viable Content Framework

Transcript & images: Hey folks! Welcome to this the second episode of my Learning Science podcast - it’s great to have you here.For the new subscribers who have joined us since last week - welcome! I’m Phil and I’ve been researching & experimenting with online & hybrid learning for over 20 years now, as an academic as an instructional designer and, more recently, as a Chief Learning Officer, VP Learning Product & founder in the world of ed tech. Today, I’m going to be talking about a concept that I call Minimal Viable Content - a simple but highly effective approach to designing content which has enabled me to significantly improve learner outcomes while increasing my design velocity and reducing my design costs. Sound good? Let’s dive in!Content Addiction A question I get asked often is: what is the #1 problem with online learning experiences? My answer: we almost always deliver too much content. Earlier this year I ran rapid review of online courses delivered across K12, HE & corporate L&D globally. I found that, on average, online courses have an 80/10/10 break down:* 80% content - videos, audio, images, texts etc * 10% activity - multiple choice questions, drag-and-drop etc * 10% feedback - automated or responsive feedback on activities This is pretty bad news for everyone: * Learners: are passive, experience cognitive overload and either drop out or (if the experience is mandatory) complete it through mindless box-ticking. In the process, they learn little, if anything, about what they hoped to learn. * Learning Designers: spend on average 60% of their time creating, reviewing & editing content. They have limited time for much else, including learning science. * Organisations: spend a tonne of money creating content - most often video content - for little Return on Investment. Why are we so content-happy? If content doesn’t correlate to learning, why do we spend so much of our time, energy & resource on content creation? I think there are three main reasons: First, habit: at school & university, content-heavy “sage on the stage” approaches to teaching are king. Online, we reproduce this knowledge transfer pedagogy primarily because it’s what we know (and it worked for us, right?).Second, technology: as a result of #1, we have built online learning technologies to deliver knowledge transfer pedagogy. Overwhelmingly, online learning platforms - from Blackboard, Moodle & Canvas to EdX, Udemy & Masterclass - are built to deliver digitised content + knowledge checks. The proliferation & popularity of content authoring tools like Captivate, Storyline & Rise confirms and continues to compound our content-centric approach to online learning.Third, training: as a combined result of #1 & #2, most of the training & support we receive as learning designers is about how to use content authoring & content-first technologies. As learning designers, content creation is what we do. Introducing: the Minimal Viable Content Framework Over the last few years, I have run an interesting rubber-band thought exercise during every learning design process I have run. I call it the Minimal Viable Content framework and it has three steps: Step 1 Diverge: what would amazing content look like for this learning experience? This typically results in a brainstormed collection of really exciting but mostly out-of-budget, often high-tech ideas: things like interactive video content, virtual & augmented realities & gamification. Real-life example: Problem solving skills - “We could create a Roblox-style simulated gaming environment where students solve problems to progress through various levels.”Step 2 Converge: what would this learning experience look like if we didn’t create any content at all? What if we weren’t able to create or curate any content, and instead could only set problems for learners to get to a solution under their own steam? This typically results in a sea of confused faces followed by a series of lightbulb moments. Real-life example: Problem solving skills - “We could ask learners to solve a defined problem using whatever resources they have access to and prefer, e.g. Google, YouTube etc. This would help us test how well they frame & explore problems, as well as how well they solve them. It would also make the experience more personalised and adaptive.” Step 3 Decide: what is the Minimal Viable Content required for this learning experience to hit its outcomes? Typically this shifts from defaulting to “a series of short video lectures covering X topics” to short prompt content (text, video, audio) to: * Set up problems or challenges* Provide scaffolding for problem solving * Provide feedback once the problem is solved The Impact of the Minimal Viable Content Approach Typically, using the MVC exercise results in a shift from 80/10/10 to 10/80/10 designs, i.e. * 10% content - videos, audio, images, texts etc (often curated, rather than created) * 80% activity - solving problems out in the world (not just in-platform)* 10% feedback - automated or responsive feedback on outputs from activities In practice, this means the creation of learning experiences which are optimised for real skills & knowledge development. Compared with content-centric approaches, courses designed using the Minimal Viable Content Framework are more likely to:* be active & participatory* engage learners* deliver a personalised & adaptive learning experience* effectively manage learners’ cognitive load This is good news for: * Learners: who are active, engaged & learn through doing * Learning Designers: who spend on average 30% of their time creating, reviewing & editing (or curating) content. This means they have more time to dedicate to other aspects of their job, including how to optimise their designs for impact. * Organisations: who spend less money creating content and get a better Return on Investment. I’d love to hear and learn from you! How might adopting a Minimal Viable Content mindset impact your design process & course designs? ______Want to learn more about the science of great learning design? Apply to join the 3-week Learning Science Bootcamp and work through an end-to-end design process with me and a cohort of people like you.The next cohort kicks off July 11th & applications are open now - come and join me!Happy designing! 👋 Get full access to Dr Phil's Newsletter, Powered by DOMS™️ AI at drphilippahardman.substack.com/subscribe

Online learning is broken - is a new approach to learning design how we fix it?

Transcript & images: Hey folks! Welcome to this my first podcast as part of my Learning Science Newsletter - it’s great to have your here.For the new subscribers who have joined us since last week - welcome! I’m Phil and I’ve been researching & experimenting with online & hybrid learning for over 20 years now, as an academic as an instructional designer and, more recently, as a Chief Learning Officer, VP Learning Product & founder in the world of ed tech. Today, I’m going to be talking about some of the fundamental problems that we face with online learning & exploring whether (or how far) we can solve this problem through changing how we think about and execute learning design processes. So, let’s go! In the last decade, millions of us - from teachers, academics and learning designers to people in workforce training and individual content creators who want to share what they know and love with the world - designed online & hybrid courses. This is great news, but there's a problem: online learning is broken.More and more platforms are being built every year, enabling more and more people to deliver learning experiences online. However, the vast majority of the learning experiences that we create fail to engage, inspire & inform learners in the way that was intended: the average completion rate for online & hybrid learning experiences which aren’t mandatory continues to flatline somewhere between 7% & 14%, meaning that - astonishingly - drop-out rates for online learning sit somewhere between 86-93%. The most interesting question, of course, is why? Why, despite our growing appetite and ability to build them, are our online learning experiences failing to meet anywhere near their potential? Often it’s the technology & sometimes the content which get the blame: if only we had better online platforms and if only it was easier to make dynamic content more quickly, then things would be different and online learning would change the world.  While there is certainly an amount of truth to The Great Delivery Problem, there is a more fundamental but less obvious problem at play which - if we can resolve it - has the potential to transform online learning as we know it, regardless of the technology that we use to deliver it.  Introducing: the Great Learning Design Problem Put simply: there is a breakdown in communication and a lack of intersection between the science of learning on the one hand & the art of learning design on the other. On investigation, it doesn’t take long to see why. Learning science research is very expensive: articles are locked behind multiple paywalls which mean it’s not readily accessible, especially so for those outside of academia. Finding & reading research is incredibly time consuming: it often takes postgrad-level skills to source the right content and then to translate research data into actionable design practices. If you do manage to access the research, read it and distill it into your design practices, the constantly evolving nature of the world of research means that your work to keep on top of it is never done. What all of this inevitably means is that what we know about how humans learn is not being translated into learning design processes or practices, which helps to explain the 7%-14% average completion rates. As I know first hand as a learning designer, in the absence of learning science, learning design tends to become a bit of a finger in the wind exercise; we’re forced to make critical design decisions about things like how to write and sequence outcomes, how to balance content and activity and how to select modes of delivery based on a combination of instinct, precedent & - if we’re honest - an amount of sheer guess work. But, interestingly, things are beginning to change. A small but important trend is underway which has started to reframe The Great Online Learning Problem as one of pedagogy rather than technology, and one of design rather than delivery. The DOMS™️ Learning Design FrameworkOne example of this trend is the emergence and early success of the DOMS™️ Learning Design Framework - DOMS™️ being the acronym for the four stages of the process - Discovery work, Outcomes writing & sequencing, course Mapping & course Storyboarding. For those who are familiar with existing learning design processes, DOMS™️ is essentially a post-ADDIE & SAM design framework for a pedagogy-first world: a simple, step by step method which makes it easy to apply the science of learning to the art of learning design. So how does DOMS™️ work in practice? Well, at every stage of the 4-step design process, the DOMS™️ Framework provides people who design learning experiences with two things: * A summary of peer reviewed research related to that stage. * A set of design principles & concrete design practices, based on the research. So, for example, at the Outcomes stage of the process, the DOMS™️ Framework provides a summary of what the research says about effective outcome writing & outcome sequencing and it also explains how to apply the science of outcome-writing & sequencing to your design decisions in order to optimise your learning experience for leaner engagement, motivation & achievement.Discovery: a summary of what the research says about effective discovery & how to apply the science of learner profiling to optimise your design for learner engagement & motivationOutcomes: a summary of what the research says about effective outcome writing & sequencing & how to apply the science of outcome-writing & sequencing to optimise your design for leaner engagement, motivation & achievementMapping: a summary of what the research says about effective course formats and flows & how to apply the science of sequencing to optimise your design for engagement, motivation & achievementStoryboarding: a summary of what the research says about how to select & design content, activities, assessments & feedback which optimise for learner engagement, motivation & achievementThe DOMS™️ EffectSo, does the DOMS™️ Framework work? Well, DOMS™️ has been tested in a number of contexts - including higher education, K12, workplace L&D and with individual content creators - with some great success. In each case, using DOMS™️ resulted in 10X improvements in levels of learner engagement, retention, achievement & satisfaction, compared with designs created using alternative design processes like ADDIE & SAM or experiences designed using no defined process at all. Thanks to its simplicity and clarity, DOMS™️ has also proven to increase “time to design” - i.e. the speed of the end to end design process - by between 5X and 10X, when compared with alternative design processes.Initial findings therefore suggest that DOMS™️ is delivering on its promise to solve the The Great Design Problem & help fix online learning by helping people who design learning experiences to connect the science of learning with the art of great, high-impact & truly-transformative learning design. Try the DOMS™️ Framework for Yourself If you’d like to get hands on and try out DOMS™️ for your design work, you can join my Learning Science Bootcamp: a three week, hands-on, cohort based course during which you will design (or redesign) an online or hybrid course of your choice using the DOMS™️ Framework supported by me, guest experts and the rest of our cohort. The next cohort kicks off July 11th & applications are open now - come and join me! For now, happy designing! 👋 Get full access to Dr Phil's Newsletter, Powered by DOMS™️ AI at drphilippahardman.substack.com/subscribe