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In How People Learn (Kogan Page, 2019), Nick Shackleton-Jones does several things. He introduces a model of learning, and proceeds to use that to evaluate current approaches. He then provides prescriptions to improve practices. The end result is that he gets some of this right and some of this wrong, but the upside is that what he gets right is useful, so it’s worth considering the book as a whole.
The beginning points out some anomalies between what we’re told about learning, and empirical evidence that seems counter. Shackleton-Jones provides examples of how we don’t seem to learn the way predicted; some things fade quickly, and others seem to stick. He points out that the ones that stick are emotionally charged. We remember great failures and great successes. We remember things we care about, and struggle with things we don’t. Is there something important here?
Shackleton-Jones proposes a different model of learning. In the third chapter, he introduces his Affective Context Model of learning. This model states that we only store the emotional component of learning, and reconstrue our memory from that basis. If we have no emotional component, we can’t reconstruct. Thus, his proposal is about only teaching what’s meaningful, and other things can’t be worth the effort. This is, to say the least, controversial.
The alternatives are characterized as push or pull. If individuals care, the minimum is all you need to do; give them what they need and they’ll figure it out. This is pull. Push is for “where people do not care about something, we need to provide affective significance.” Here he addresses a concern near and dear to my heart: making it matter. He talks about nuances in learning from mistakes, the role of story and observing, and generally designing experiences.
He goes on to then assault the education system and workplace learning approaches. With a chapter title like “Education: The Great Learning Prevention Scheme,” he’s clearly more interested in being provocative than diplomatic. He rails against information dump and knowledge test, pointing out that there’s little evidence it works. Rightly so.
His prescriptions are to use resources when you can, and then design for meaning when you can’t. You need to get right in and understand the situation, a very design-thinking approach. He has his own 5DI model that provides a structure for approaching learning design in a way that looks at the whole situation and finds ways to support appropriately.
And most of this is right. We do need to minimize courses and go to resources first. This is increasingly true in our digitally-enabled environments. And yes, our schools have problems as does corporate learning. We do want to tap into intrinsic interest and make learning meaningful. The interesting thing is that he gets there from a flawed premise, but it’s not ultimately a death knell to his argument.
The problem is that his model has flaws. What do you reconstruct with? If you just store your emotional structure, what lyrics do you have to guide you when you sing a song again? With just emotion, where are the linguistic hooks? We need long-term memory for knowledge to scaffold our performance as we internalize. And there’s strong evidence that’s what’s stored isn’t just emotions. We can’t just be emotionally engaged, we need meaningful application and feedback, and that feedback includes models, examples, and more. He implicitly recognizes this with the emphasis on story, but it’s not enough.
Shackleton-Jones’ “straw man” characterizations of education and organizational learning, while pointing out real flaws, also don't reflect the spread of approaches that actually work. The recognition of how “situated” cognition is, the reconstruction in every situation based upon context, is known, and is a factor in most modern frameworks for learning design. Michael Allen’s CCAF (Context – Challenge- Activity – Feedback) and Cathy Moore’s action mapping, amongst others, focus on meaningful action. The fact that good intentions are hindered by state standards or organizational needs for efficiencies is, admittedly, a problem. The thing is, we have frameworks that point in the same important directions, but have a sounder basis in research. While we don’t see it in practice, it’s not because science doesn’t know better.
Further, Shackleton-Jones’ emphasis on resources isn’t unique, it can be seen in performance consulting approaches. Such approaches suggest doing a gap analysis, and a needs analysis, getting in to the real problem. He rails against objectives, yet the fact that they’re not well done doesn’t preclude you from a need to determine what the needed core change is. We need to focus on the core decisions to design experiences. Again, the need for meaningful change has been established in a number of known frameworks.
Yet there is considerable value here. His insights into framing just why we can go minimal when we know and care is helpful. It provides another rationale to support “resources before courses,” which isn’t seen enough. And his emphasis on the importance of making it meaningful, in lieu of much focus in systematic approaches, is a substantial boost to our traditional designs. This is particularly important in eLearning, where such motivation can be harder to imbue. This is a valuable call to arms to rethink our approaches. Even though there are better foundations, there aren’t near enough clarion calls for considering the emotional side of the learning and performance experience.
Further, the book is a nice read. He’s entertaining with his stories, and a distinct personality emerges that is irreverent without being irritating. And the different perspective is useful to challenge institutions that need some upheaval. His recommendations are, largely, right. This book should guide your learning design as much as it starts you thinking about performance consulting. So, this is a worthwhile read to complement an enlightened awareness of the science of learning. I wouldn’t start here if you don’t already have a background in theory and research. Or skip the first and second chapter, and for “affective context model,” substitute “learning science,” and you should be roughly good.
It’s hard to recommend this as a starting piece. It’s easy to recommend to those who have some background and can weigh the statements accordingly. The book is fun, insightful while also being inciteful. It’s sound about what it advocates, just not why. And I have mixed feelings about that. I can’t give an unqualified recommendation, but I certainly can give a qualified one.
Clark Quinn leads learning system design through Quinnovation, providing strategic solutions to Fortune 500, education, government, and not-for-profit organizations. He earned his Ph.D. in applied cognitive science from the University of California, San Diego, and has led the design of mobile, performance support, serious games, online learning, and adaptive learning systems. He's an internationally known speaker and author, with four books and numerous articles and chapters. He has held management positions at Knowledge Universe Interactive Studio, Open Net, and Access CMC, and academic positions at the University of New South Wales, the University of Pittsburgh's Learning Research and Development Center, and San Diego State University's Center for Research in Mathematics and Science Education.
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https://doi.org/10.1145/3362067
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Tue, 06 Apr 2021
Thanks for the great content. I will also share with my friends & Once again thanks a lot.Post by Sukhada
Wed, 05 Aug 2020
Thank you for the Great informationsPost by happy_world