The strategic value of learning design

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It’s an obvious statement, but technology can both help and hinder learning. And if we’re not careful, it can create more barriers to learning than it reduces.

In this article, we’re going to look at how cognitive load affects learning, and the role that technology plays in increasing and reducing cognitive load. 

The impact of cognitive load on learning 

When we’re learning something new, we experience something called ‘cognitive load’ as our brain tries to make sense of the new information. Cognitive load refers to the idea that our working memory can only hold a limited amount of information. When we’re thinking about using technology in learning design, we therefore have to ask ourselves how much ‘load’ the technology will place on the learners. If they’re very familiar with the intended technology, the load will tend to be less. The aim is to minimise the additional cognitive load produced by asking learners to use a specific technology.

Imagine you’ve just bought a new television. What you want is to be able to watch programmes on it – we can call this your learning goal. But before you can watch a programme, you have to work out how to switch it on, navigate the menu system, connect it to the internet, tune the channels, adjust the colour and turn up the volume. All of these steps are barriers between you and your learning goal – you have to use effort to overcome all these barriers before you can do that. Once you do, your reward is that you can finally watch television.

The same principle applies in video games design. If a video game is too easy, we stop playing it. But equally, if it’s too difficult, we give up and stop playing it. The art of video games design is to ensure players are absolutely clear about the goal they’re trying to achieve, that this goal remains crystal clear throughout the game, and that at all times the difficulty level is just hard enough to motivate them to keep playing.

Effort and reward

The dynamics of effort, reward and cognitive load are integral to effective learning design. Research in cognitive science shows that if we have to use effort to learn something, we’re much more likely to remember it. If it’s too easy, the information doesn’t stick in our long-term memory. If it’s too hard, we won’t achieve the intended learning outcomes. But we only have a limited amount of effort – we won’t keep trying to learn something indefinitely.

When we introduce technology into a learning design, the key thing is this: the technology is not the intended learning outcome. While it is almost inevitable that using technology will increase the cognitive load that learners experience, we don’t want learners to use up all their effort figuring out how to log in to a platform, or trying to work out what button they need to press to send a message. We want them to use their effort to complete the task we’ve set them so they can achieve the intended learning outcomes. 

So how do we ensure that technology doesn’t significantly increase cognitive load and get in the way of learning? Well, there are four key principles we can use to guide us, and they are Relevance, Accessibility, Security, and Usability. Let’s look at each of these in turn.

Relevance

Firstly, we should always ask whether digital technology is relevant to the learning design. Is it really necessary that we use technology at all? What will it add to the learning experience? This is where it helps to ask ‘what aspect of learning are we trying to affect with technology?

For example, we might want to deliver content to learners before a session, in which case we need a tool to broadcast content to them. Or we might want to provide a real-time online group-work activity, in which case we’ll need an online classroom tool that enables us to facilitate interaction so learners can socially construct new knowledge. Or perhaps we want them to discover new ideas and do research into some threshold concepts, in which case we’ll need a tool that supports discovery and enables learners to capture and share their learning.

The point is once we’ve established the aspect of learning we’re trying to influence, we can then start looking for tools that will have the desired effect and put together a list of options.

Accessibility

After we’ve created a short list of potential technologies, the next questions are ‘can all learners access them’ and ‘what barriers to learning do these technologies create?’ This is the ‘accessibility’ criterion, and we can use it to evaluate the suitability of each technology on our short list. If a technology prohibits some learners from participating, we have to instantly discount it. This can often happen when learners are in organisations or countries that restrict access to specific tools.

Once we’ve checked that all learners can use a technology, we can then investigate the barriers to learning it might create. For example, we might want to share videos with learners, but if a platform doesn’t allow subtitles or closed captions we have to strike it off our list. Then we might evaluate whether the platform works effectively with screen readers, which are tools that read out text for people who are blind or who have low vision.

We also need to consider the device learners will use to access the content and activities. If we assume they have a laptop, we may be mistaken – it could well be that they will be accessing everything on a phone or a tablet. So we need to ensure the platforms and technologies we choose work effectively regardless of device. 

Security

This leads us on to our third criterion, which is security. Once we’ve established that all learners can access the content and activities, we must do everything we can to ensure their data is stored securely. There are no hard and fast rules here, other than that as learning designers we need to do our research into the past performance of each technology.

Depending on your situation and location, you may need to work within international policies such as the General Data Protection Regulations, or GDPR. If you’re working in an organisation, you’ll also need to stick to internal policies and regulations which may further restrict the digital tools you’re allowed to use. 

Usability

Once we’ve satisfied these three criteria, the fourth and final question is ‘what quality of learning experience does each technology produce?’ There are so many digital tools that can be used for learning, but it’s all too easy for technology to get in the way of learning. If a digital tool is working effectively in a learning design, it should be mostly invisible.

Our learners may well be finding the content hard enough to learn, so the last thing we want is for the technology they’re using to make it even harder. We have to bear in mind that most of the digital technologies available today weren’t designed with learning in mind – it’s up to us as learning designers to make informed decisions and choose tools that enhance the learning experience, rather than get in the way of it.

So if we want to make sure technology doesn’t get in the way of learning, we need to focus on learning first, and technology second. It’s the learning outcomes that are important, not the technology – the technology is just a vehicle that takes learners to the learning outcomes. And we can use these four principles of Relevance, Accessibility, Security and Usability to help us identify digital technologies that are appropriate for supporting learning. 

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Thank you to:

• The Unsplash community for a fabulous photo!

Some two months after the launch of OpenAI’s ChatGPT, a LinkedIn tech editor asked how I felt about students writing essays with it. Swiftly I replied: ‘There’s a whole industry out there writing essays for students; ChatGPT will do this for them for free.’

‘Free’ comes with caveats: this new AI tool will almost certainly need discriminating prompts and requests for adjustments from a student to produce essays that might pass. And the undiscriminating student may find the answer they have submitted via ChatGPT isn’t quite what the question asked for (Chat doesn’t know whether the answers it generates are true – though the next generation of AI probably will). It may also be wearily verbose where it could usefully have been prompted to cut down or summarise. It will need to be prompted to produce quotes, but it won’t be reliable on references.

Putting ChatGPT to work

There’s quite a lot here for a student to get their mind round, and they may be developing critical judgment and problem-solving skills of a high order while using ChatGPT. In this connection, Lee Griffin of the University of Warwick writes on LinkedIn:

“Make it part of the question. “Use AI to generate an answer to [question]. Now critique and reference it… Work with the thing. Don’t fight it.”

So good – and much better that than the New York City education department’s decision to ban it from all their schools: instead, teach children/students how to get the best out of this new resource and recognise the critical thinking skills it can be used to develop. These NYC children will be using AI in the workplace eventually, and to come out of school without an understanding of its potential seems pretty short-sighted – to say the least.

The focus so far has been on cheating in essays – wringing of hands and catastrophising – but ChatGPT can offer much more of value, and wouldn’t we want our students to have this and understand how to get the best out of it?

A dyslexic early adopter of ChatGPT has been using it to speed up and clarify the reports he needs to produce. He may use it to rewrite something he’s produced, asking it to make his text easier to read or shorter or maybe catchier; he uses it to find language that is more appropriate or words that he can’t immediately summon up. He recently asked it to summarise a report in three sentences. Done immediately. This man is an entrepreneur; ChatGPT has freed him from some of the tiring, sometimes tiresome but necessary communication that takes him away from his creative practice.

Benefits and risks of ChatGPT in learning design

As with any new technology, ChatGPT presents both benefits and challenges for learning designers and academics. Let’s start with the positives: ChatGPT will hopefully improve course documentation. For starters, those sometimes dreary, puzzling paragraphs in course and module handbooks that could be so much clearer and snappier – ChatGPT can transform them. Instantly.

At the next level up, will it design a course for me with a bit of raw material input and a couple of prompts? The answer is, well, not really – yet: it may give you a basic design of some kind if used judiciously, but you’ll certainly have to put in some time on developing this starter pack if you’re to produce a course of any value.

Sean McMinn of Hong Kong University of Science and Technology set ChatGPT the task of creating learning outcomes for an English course using prompts on the taxonomy for these outcomes. He then asked the tool for ‘draft assessment plans, rubrics and lesson plans based on the ILOs’. He goes on:

“Within minutes, I had the makings of a new course – or the concept of one, anyway. Results were only as good as the prompts I gave, and I still needed to rely on my knowledge of instructional design to evaluate the content within a Hong Kong university context.”

But there are clearly some new negatives to navigate in this brave new world. Simon Atkinson notes that any algorithm is only as effective as the data used to train the model, and consequently there is a very real danger of reinforcing existing biases. If the data that ChatGPT draws on is biased, and it almost certainly will be, its output will be biased. This increases the risk of unfair treatment of particular students, or groups of students. As learning designers, we will therefore need to spend more time on the very human task of ensuring that learning outcomes created by ChatGPT will not increase discrimination. 

In a tentative review of the field of generative AIs, Gozalo-Brizuela and Garrido-Merch´an provide a more technical insight into the inner workings of these algorithms. They too highlight the issues inherent in training these models, and the risks of bias creeping into to decision-making. While many people may be concerned with the end of the essay, the dangers of bias represents for me a much more serious risk as these tools become more widely used.

The genie is out of the bottle

ChatGPT reminds me of the genie in the story of Aladdin. It’s out of the bottle, and you can’t put it back in. But (at least for the time being) its wish is our command. Sometimes us humans invent something that creates a paradigm shift, and then we have to learn to live in the new paradigm. ChatGPT speeds up the process of creating text, no doubt about that. But it’s a long way from telling me whether what the text it’s produced is useful and relevant to what I need. 

As learning designers, ChatGPT shifts our work more towards asking better questions, and imagining more fully the human experience that could be created by the outcomes produced. It moves our focus more towards empathising with our intended learners, and on spending more time developing inclusive and equitable access to learning experiences. And that is no bad thing.

So, on the one hand, perhaps – boo; but on the other, phew: I haven’t been techno’d out of a job yet.

You might also like:

• McMinn, S. (2023) ChatGPT killed the classroom star: AI’s rise means it’s times to rethink teaching and testing. South China Morning Post
• Atkinson, S. (2022) Empowering learners for the world of AI: a reflection.
• Gozalo-Brizuela, R. and Garrido-Merch´an, E. (in press) ChatGPT is not all you need. A State of the Art Review of large Generative AI models.

Thank you to:

• Ray Martin for help with researching and producing this article.
• Taha on Unsplash  for a fabulous photo!

What is the point of education in a world of less work?  How can educators and their institutions prepare their learners for a future transformed by AI? How do we educate for irreplaceability? And what is blockchain, anyway?

In this episode, our guest is Tomer Garzberg, founder and CEO of GRONADE, an enterprise growth labs and future-of-work company in Sydney, Australia. Tomer is a technology entrepreneur and innovation advisor, and has worked with some of the world’s most notable companies, blending the worlds of data, AI, behaviours and creativity. An advocate for disruption, Tomer likes to ask big questions about what the future of work is going to look like. He’s an IBM Global Entrepreneur, TED speaker and innovation advisor, and he believes we should be thinking differently about education if we’re going to prepare graduates for the real future of work.

As a confirmed advocate of the potential for artificial intelligence to improve the human condition, Tomer’s argument is persuasive. Whether you agree with him or not, his perspective provides a valuable perspective on the opportunities and challenges that technology will bring humans in the near future. Among the many insights that emerged through our conversation with Tomer were:

• technology has one role: to shorten the distance between fulfilment and desire
• in a world transformed by AI, we’ll need more skilled translators, who can communicate the benefits of new technologies more simply, to more people, and more immediately.
• in order to become more human, we need to resist comfort zones, enact our curiosity and interrogate our behaviours.

Enjoy the podcast!

What metaphors might best describe some of the side-effects of the pandemic on universities, their staff and their teaching?  How can educators and their learners keep up with the increasing rapidity of new knowledge, and what do we mean when we say, ‘beware the creepy treehouse?’

In this episode, our guest is Jeremy Hunsinger, Associate Professor of Communication Studies at Wilfrid Laurier University in Canada. Jeremy writes and teaches about many topics including Critical Making, Critical Technical Practice, Internet Studies, Politics of Knowledge, Virtual Worlds, Interpretive Research Methods, and Interpretive Policy Analysis. But when he gave a presentation on the characteristics of the post-pandemic university, his metaphor of the dangers of creating ‘creepy treehouses’ for students meant we just had to invite him onto the podcast to tell us more.

During the conversation, Jeremy produced many lightbulb moments including:

• The idea of ‘professorial zombies’ as a consequence of digital representation and the proliferation of zoom recordings.
• challenging the model of the accelerated university, and courses as ‘career-orientated processing machines’, by moving from pedagogy to andragogy.
• how nostalgia for own educational experiences might be producing ‘creepy treehouses’ for students.
• how the move to online learning is pushing universities to do better around the student experience.
• challenging individualisation of learners through the idea of cohort admissions.

Enjoy the podcast!

If you design any kind of online learning experience, there’ll be more than a few Ding moments for you in this episode including:

• The absolute necessity for online learning spaces to be designed for visible participation to enable enthusiasm and accountability
• The need to think purposefully about every aspect of the online learning environment, just as you would do in a physical teaching space
• The value of thinking about the visible contract between teacher and learner in order to inform the expectations of the learning design
• And the importance of pushing back against established conceptions of curriculum design and delivery. These might be things like the number of contact hours, or the need to use a specific learning management system, and by pushing against these we’re more able to tailor the learning experience more closely to the specific needs of a specific group of students.

Enjoy the episode, and please share your thoughts and questions in the comments!

P.S. if you do enjoy this episode, you might also like the episode where we talked with Tom’s colleague, Paul Hoskins, about opportunities for global higher education.

If you’ve got data, it should be driving learning in your organisation. So says David James, Chief Learning Officer at Looop, where he helps businesses use a data-driven approach to learning in order to solve business problems.

In this episode, David argues how using data and a resource-based approach to supporting employees can help the traditional learning and development function evolve in response to the demands of the digital age.

Key moments in this podcast:

• The danger of L&D in organisations being simply a conga, where everyone has a great time dancing but where the development isn’t data-driven;
• The importance of providing learning opportunities that people actually need, not those that the organisation wants them to have;
• The need to remain focused on the intended outcome of development, and make the ‘learning’ as timely and efficient as possible, and
• The value of making the culture of an organisation visible to employees through the use of digital resources.

We loved David’s concept of the ‘conga’, where lots of people seem to be having a good time but where there’s little evidence of learning or change. Since we recorded this episode, we’ve noticed congas in all sorts of learning experiences from higher education to corporates to startups. 

Next time you attend a learning event, ask yourself what change the event is likely to produce in the participants. And keep an eye out for a conga…

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