Costs

The first cost to be considered is the development. Mini-scenarios, just better written multiple-choice questions (MCQs), take some practice to get good at. While there are no technical costs involved, as they just use pre-existing MCQ frameworks, they do require thinking a bit differently. Designers need to think of meaningful decisions, appropriate contexts, and consequences. Similarly, there’s the appropriate spread of the same. None of this should be different, but in our experience, the ways of thinking about changing how you write questions does take some time. Don’t forget to include time for testing.

Branching scenarios, of course, take more thinking to develop. It’s not just the thinking about contexts, challenges, and consequences, but also storyboarding the overall experience. The technology, fortunately, is likely to already exist in your toolset, except perhaps for mapping out the options. You can go further, however, and get dedicated tools for branching scenarios. That may make sense if you’re going to implement them at scale. Testing may be a tiny bit more involved, also.

Going beyond, to full simulation-driven experiences, serious games, is another matter. Here, there’s programming involved. There may be tools, such as 3D game environments, that make rendering the world easier, but you still need to capture the underlying relationships. As well as the development costs, there’re also the tuning costs. Will Wright, creator of SimCity and the Sims amongst others, once told me that the development was only one-tenth of the work, and tuning was nine-tenths. Now, he was talking about making commercially viable products, which we’re unlikely to be shooting for (we have the proverbial sticks as well as carrots). Still, tuning is part of the process.

Of course, the technologies you choose to deliver in will have costs as well. If you’re using video, it’s more costly than images and text. 3D environments, e.g. virtual reality, or alternate reality games will similarly be dearer. Even high-quality audio (which matters more than the quality of the video, counterintuitively) can be costly to produce.

Benefits

Those costs, however, are likely to be offset by much-improved learning outcomes. Really, we’re talking about achieving ‘deeper learning’. If you’re trying to achieve meaningful change, contextualized practice is your best bet next to mentored live performance (which has troubles of costs for failure and to scale). Thus, scenario-based learning is a highly desirable approach.

Learning science research lets us know that contextualized retrieval practice, where you practice as you need to perform, is the most effective approach. It’s not a full solution, in and of itself; you still need models and examples, for instance, but it’s highly likely to be the necessary adjunct. The facts are that bullet point information dumps, even when augmented by knowledge tests, are extremely unlikely to lead to behavior change. People can’t learn to do new things unless they actually practice doing the new things!

That’s what scenarios do. They provide contextualized practice where you must perform in the learning environment as you’ll need to perform after the learning. Appropriate use of technology, when relevant, offers a valuable tradeoff of cost for effectiveness.

Take Home

The outcome, the ability to do new things that the organization needs, is always the focus. The potential overhead costs are traded off against the new ability to do, and the benefits it brings. We are strongly committed to achieving the outcomes organizations need at cost-effective rates. We just want everyone to understand what leads to sustained learning, and what it takes. Scenario-based learning is a powerful, and therefore valuable tool, with very approachable costs.

If this makes sense to you, and you’d like more guidance, we’re committed to help. Feel free to reach out to us at elearning@upsidelearning.com. We’re happy to have a conversation, and to gain a comprehensive grasp of scenario-based learning dive deeper with our eBook ‘Scenario-Based Learning: The Ultimate Asset In Your L&D Toolkit’

The post Tradeoffs in Scenarios-Based Learning: Costs and Impact first appeared on The Upside Learning Blog.

Elements in Scenario-Based Learning

Designing scenarios involves getting all the elements correct. This includes the setup, the options, and the feedback. There are nuances on each element that can spell the difference between effectiveness or not. For example, it’s instead the setup, decision, consequences, and outcomes.

For one, the initial setting has to meet two criteria. First, it has to be a situation that’s a natural application of the decision-making. The performance objectives established should clearly be represented in the choices of action the learner chooses from. The second element is that learners have to recognize that the situation portrayed is relevant to them. It doesn’t have to be the exact same situation they’ll be in, but they should recognize that it’s a legitimate application, even in a fantastic setting.

The choices of action should be plausible and be active. That is, it’s not identifying the choices, but making one. The alternatives to the right answer ideally are the ways performers typically go wrong. This serves two purposes. For one, it gives you a way to make the decision sufficiently challenging. While it’s possible for questions to be too difficult, more frequently we see questions that are too easy, and they don’t lead to learner engagement nor meaningful outcomes. The other reason is that it is better that learners make mistakes (and get feedback) in practice rather than when it matters.

Which means the feedback should be specifically related to the choice they make. If they choose one wrong answer, it’s typically for a different reason than they’d choose another wrong answer. Thus, the feedback should be specific to the mistake, and provide the opportunity to remedy the misconception before it occurs in performance. Obviously, bad choices aren’t useful to support learning.

We should also match the scenario structure to the performance needed. If the decisions are tied together, a branching structure makes sense. Individual decisions can be handled through mini-scenarios, which can also serve as scaffolding to support developing the component skills before pulling them together in a larger scenario. Linear scenarios make sense when you need to control the decisions learners will experience, for instance for assessment.

Another success factor is having a sufficient quantity of scenarios, with increasing complexity, and set in an appropriately broad series of contexts. Our goal here is to ensure that we’re developing the ability to make decisions to a level sufficient to support the necessary performance and to develop sufficient confidence on the part of the learner to be willing to attempt when it matters. We also have the goal of ensuring that we’ll successfully prepare the learner for all the situations they’ll face. To do so requires choosing an appropriate set of contexts across which they see (both in examples outside the scenarios as well as the ones experienced within). This is coupled with increasing the challenge to the point where they are facing the level of difficulty they’ll see after the learning. We want them to generalize from those to a transferable ability to apply at a capable level where useful.

That focus on contexts across examples and practice is an important consideration for supporting transfer. For example, while the settings need to meet the criteria above, the decision needs to articulate the underlying thinking, before showing the consequences of the choice and the longer-term outcomes. We want to explicitly refer to the model, and the story should be compelling, also.

Experience in Scenario-Based Learning

Getting the elements right comes together as an experience. Generating the overall flow is more than just the elements, but it’s also some overarching principles. These, too, matter to achieve success.

First, we should not assume that our first draft will be perfect. While we have good principles upon which to design, there are gaps, and people are complex. As such, it should be an expectation that we should test the design, and then tune the experience. We may not need a commercial level of play, but we need learners to not resist nor reject the experience.

Our goal should be to address emotional elements as well as educational effectiveness ones. ‘Emotion’, here, is a shorthand for motivation, anxiety, and confidence. As stated above, we want learners to leave sufficiently confident. As such, we need sufficient quantity and a low level of anxiety. Too much anxiety interferes with learning, and so even if we eventually need learners to perform in stressful environments, we start with a low level. As skills are mastered, then we can address performing those skills in stress.

We also want learners to be motivated, ideally as close to their live level as possible. That comes from ramping up the story a bit: not just by working on an ordinary situation, but by making it extraordinary. There should be an initial hook, beyond the scenarios that engages the learner’s interest, delivering a valid ‘What’s In It for Me’ proposition, and the scenario should deliver on that promise. That comes from ensuring that the challenge is balanced, the context plausible, and the feedback impersonal but relevant.

We also want to use the appropriate technology for the task. We don’t want to over-invest, so we should save something like VR for when immersion is needed, but we do want to appropriately use media — video, graphics, audio, etc. — in ways that suit the need. Similarly with choosing branching or full simulation engines.

That also includes how the experience is situated in the larger context. In addition to an introduction hook, if there’re tools used in performance, they should appear in the learning experience. Similarly, the examples should complement the practice in creating the overall experience.

With all that, there’s also a benefit to minimalism. Extraneous details undermine educational effectiveness, so you want to convey context but not overload the details. Allow our knowledge of how the world works, such as office events, to fill in the gaps instead of conveying all the details. Just as a good book or movie doesn’t tell you everything, let the action do so as well. Reining in detail is cognitively good, and good on the budget as well.

Mastering scenario-based training involves understanding the intricate elements that make it effective. This blog encapsulates key success factors, emphasizing the importance of context, choices, feedback, and the overall experience. Ready to delve deeper into this impactful approach? Download our eBook, “Scenario-Based Learning: The Ultimate Asset in Your L&D Toolkit,” for comprehensive insights that will enhance your learning strategies and maximize learner engagement. Explore the power of scenarios and take your learning and development efforts to new heights!

The post Scenario-based learning: Success Factors first appeared on The Upside Learning Blog.

Branching scenarios provide rich practice in situations where more than one decision is embedded. In this case, it was about dealing with upset and emotional people under drastic circumstances. This is a classic situation where such scenarios provide safe practice before it matters, keeping skills active for an uncertain time of deployment. Let’s dive into a case study which uncovers the role of branching scenarios in shaping immersive learning experiences.

The context

As a major international airline, the client organization in picture (Name withheld) has a natural focus on safety and an enviable reputation. Regardless, accidents happen. When they do, how an organization responds is critical to maintaining a reputation for excellence.

In this case, there’s a Deployed Response Group, and as part of that group, a Special Assistance Team is responsible for dealing with victims’ families and the survivors. This is, not surprisingly, extremely sensitive work, and practice is critical to be prepared.

The underlying model is to empower the individuals to make choices, rather than do so for them. Thus, the goals are to help these people to:

• Express their emotions and needs
• Understand what is happening
• Understand what needs to be done
• Get access to information and people
• Know what options they have
• Feel connected

How is this to be done?

The solution

As part of a larger training, branching scenarios were seen to be the appropriate practice. The whole solution included, beyond the necessary content, knowledge checks to assure understanding, and then branching scenarios as practice across a variety of different families.

The use of multiple branching scenarios accomplished several goals. For one, it provided sufficient practice for this critical skill set. Second, it provided sufficient variety to allow individuals to abstract the underlying principles and develop a generalized understanding.

While the structures weren’t exactly as pictured, as this was pre-consultation with the client, they’re indicative of the structure. The scenarios used multiple forms of feedback. In addition to the dialog from the interactions, there was also body language portrayed. This reflects the clues that need to be assessed and used to determine courses of action.

The visual design was kept clean, with an emphasis on the people and the dialog, the critical elements. Other context was conveyed but muted. The result was a simple but critical visual approach.

The outcome

This program is used as a refresher after the training. An important result was that a majority of the attendees commented that the experience of the digital solution was exactly the same as they had received in the classroom session. It also won Brandon Hall Group HCM Excellence and LearnX awards.

The situation is an example of when learning isn’t known when it’ll be needed, but it’s absolutely important when it is. Thus, it needs to be kept active. Scenario-based learning here provides the necessary practice to keep active the necessary learnings so that if and when such skills are needed, they’re available.

In the realm of emotional support during critical incidents, branching scenarios prove invaluable for training, as seen in a major airline’s Special Assistance Team. This method immerses individuals in diverse scenarios, bridging theory and practice through nuanced visual design and realistic feedback. With accolades and positive feedback affirming its efficacy, scenario-based learning stands as a powerful tool to maintain and activate crucial skills. Explore its potential in our ebook,Scenario-Based Learning: The Ultimate Asset in Your L&D Toolkit and discover its impact on skill readiness.

The post Case Study on Branching Scenarios first appeared on The Upside Learning Blog.

The context

As a major regional financial institution operating across multiple countries, the client organization (Name withheld) represents the economic safety of a considerable proportion of the world’s population. As such, it feels incumbent to represent the finest mechanisms for behavior.

In this case, the issue is company culture. There are three major standards for behavior that are required, and for employees to adhere to. These three areas are to be one unified community, to be focused on measurable results, and to put customers first. These areas of focus must be demonstrated in interactions with a wide variety of stakeholders.

The solution

Scenario-based learning was selected as a viable way to not just provide information but to require demonstrating the behaviors in action. A variety of media were used to present information, and knowledge was assessed with multiple-choice questions.

Mini-scenarios were used to ask employees to act in ways consistent with the cultural values. Learners were presented with situations, and had to choose how to respond. The choices were slightly indirect, but asked them about actions, not just categorizations.

The outcome

Cultural changes are hard to measure. Moreover, client organizations both may not know how to do so and may also be reluctant to share data with a vendor. Still, there are indicators of success.

In this case, over 95% rated the experience with four or five stars, indicating a high degree of acceptance. Moreover, the bank chose this course as their standard for eLearning going forward. Learner acceptance isn’t always a good indicator of utility, but too often, that’s the best that’s available. The project also won multiple global awards.

In conclusion, the implementation of scenario-based learning in addressing the complex issue of company culture at the major regional financial institution (Name withheld) proved to be a resounding success. While measuring cultural changes can be challenging, the high learner satisfaction rate, with over 95% of participants giving the experience four or five stars, signifies a strong acceptance of this innovative approach. Furthermore, the organization’s decision to make this course their standard for eLearning underscores its effectiveness. This project’s recognition with multiple global awards further solidifies the impact of scenario-based learning.

Eager to harness the full potential of this powerful learning tool in your L&D toolkit? Look no further. Our eBook, “Scenario-Based Learning: The Ultimate Asset in Your L&D Toolkit,” offers an in-depth exploration of the strategies and insights that lead to remarkable success and can help unlock the potential for your organization’s growth and development.

The post Case Study: Mini-scenarios first appeared on The Upside Learning Blog.

Standard Authoring Tools

The first thing to know is that mini-scenarios are just better-written MCQs. Thus, you can use your regular authoring tools (which all have MCQ abilities), with the caveat that they must support different responses for all choices. You can use media, such as images, comics, or even video to support your contextualization, as well as audio and/or text. There are, of course, the usual challenges associated with each, for instance with video the details required in scripting and filming, as well as the costs for revising, can be a barrier.

Moreover, most authoring tools also make it easy to develop branching scenarios. They may need support in terms of keeping track of the branches, but they will provide support. This is good news because branching scenarios are a great solution to create contextualized learning. They similarly can use the various media, though developing branching video can get problematic.

In both cases, then, you don’t have to invest in new software to get effective learning experiences. You may have to invest to find out how to develop these structures in the tool, but they are doable. Some even have the ability to carry variables forward, so you can do sophisticated examples.

Branching Media

Beyond general-purpose tools, there are tools dedicated to creating branching experiences. This holds true across media. Industrial strength tools make sense when you anticipate creating scenarios at scale.

Video is a powerful medium for creating context, and thus is a potent tool for creating scenarios. While the issues above in cost are true, there are dedicated tools to make it easier. There are tools for all types of branching scenarios and ones dedicated to video.

Of course, you can use more powerful general-purpose tools, with tradeoffs. For instance, you can use game engines to build branching scenarios, as these can handle rendering visuals quite powerfully. While AR and VR tools are still largely in the ‘build for this platform only’ stage, if you do know and can dictate one platform, they will work for branching scenarios.

Sim-Driven

As long as you’re using such powerful tools, you can think further, and start thinking of going beyond branching, and actually creating the underlying simulation that can drive experience. There are tradeoffs to this that are worth considering.

The major concern is the effort to develop, and tune, a simulation model, even before designing a learning scenario for it. Choosing the variables, and programming the transitions is hard enough, tuning the variables to give a plausible experience is additional work. While we don’t need to tune to the level of “pay $80 for this”, we do need to create appropriate timings and probabilities.

On the plus side, such an investment gives a lot of benefit. For one, you can provide enough randomness to support almost infinite replay. You can also provide an adjustable difficulty to support the development of a high level of ability. Simulation games, whether rendered on-screen or in an immersive environment, are powerful learning tools.

Coming Soon to a Theater Near You

One area that briefly surfaced, for a while, and should be a really effective learning tool, are Alternate Reality Games (ARGs). Here, your tasks are situated in the real world, using your existing email, phone, etc. It’s just that the design and the reactions are all handled by a script, not the real world. Imagine, for instance, practicing a sales process with virtual clients but they call you and send you email, and respond to yours! The benefits are using the tools you’ll actually use to perform. ARGs have kind of fallen away for now, but the potential remains there as a training tool.

Then, it’s interesting to explore what might be next. There have been efforts to try to get artificial intelligence (AI) agents to create games, but to date, they’ve lagged. In general, it’s hard to get AI to understand emotion, so creating and tuning experiences could be difficult. That said, an AI might be a good thinking partner, providing ideas for settings and random events, as long as the results are supervised.

While AI-generated scenarios are likely in the distant future, the tools are here now to allow us to create powerful experiences. It’s up to us to design them, but, on the whole, that’s probably a good choice.

In conclusion, scenario-based learning is a versatile and dynamic approach to education, offering a spectrum of possibilities from well-structured multiple-choice questions to immersive simulations. As technology continues to advance, the toolkit for implementing these scenarios also grows, enabling us to create engaging and effective learning experiences. To delve deeper into the world of scenario-based learning and harness its full potential, we invite you to read our eBook, “Scenario-Based Learning: The Ultimate Asset In Your L&D Toolkit.” Don’t miss out on the opportunity to unlock the power of scenario-based learning. Get your copy today!

The post Scenario-Based Learning Acronyms: From MCQ to AI first appeared on The Upside Learning Blog.

In addition to what we should do to leverage the power of scenario-based learning, there are also steps we should avoid. The ‘nos’ are things that commonly are seen in ineffective scenarios, or that can hinder successful implementation. Ensuring that these mistakes are avoided is one of the success factors to ensure the best outcomes. Some are things that are put in and shouldn’t be, and others are things that people forget to include, but should be.

Errors in Commission

In building scenarios, one of the challenges is keeping track of the branches. There’s a potential for combinatorial growth; with only two options, at 4 levels of depth, you’re already at 15 nodes. One more beyond that and you’re at 31. It keeps doubling, and that’s with only two choices at every stage! The more nodes, the more trouble you have keeping track of them, and ensuring every choice has a plausible outcome.

There are a variety of steps to take to help contain this growth. One is to have terminal nodes, where your choice ends the experience. Another is to have loops, so you can try again. The structure of the situation will dictate the response, to some extent, but if you’re finding your scenario getting long, consider breaking it into multiple scenarios, trimming it. At certain points, it becomes more feasible to build a simulation-driven experience than trying to maintain a voluminous quantity of nodes.

Another mistake is breaking the fourth wall. There’s a strong tendency to want to provide explicit feedback after every choice, or at least the wrong choice. Yet, such a reaction can break the experience.

Consequences should, and characters can provide such feedback, but only if plausible in the story. Allow the experience to conclude, with any and all consequences, then you can provide explicit feedback.

One more thing that can go wrong is developing scenarios as just practice. There are particular skills to employ, including choosing contexts that learners relate to and making believable characters. Similarly, embedding the right decisions in plausible ways and at the right level for the learners are important elements. Displaying the consequences in plausible ways is important as well. Thinking that scenarios are trivial is a big mistake. Treat them as the critical practice that they are.

Errors in Omission

One way to go wrong is to have the choices that learners choose between to be statements, not choices of action. It’s easy to set up a scenario, particularly a mini-scenario with a story, but then ask learners to determine if something’s one of several ‘things’, such as categorizing the situation. It’s a nuance, but the choices should reflect what learners should do, e.g., with such a categorization. Do you then invoke practice X, or do action Y? Make sure you’re having learners make choices that do things, not just think things.

Another problem is not having appropriate feedback. Learner choices should result in a change in the world, and that should be communicated. It’s doubly important that they see the consequences, which reinforces how the world works. That should come before any didactic feedback. Let the good consequences of the right choice, or the bad consequences of the wrong choice, be experienced! On a related note, having didactic feedback (e.g., “action X was wrong”) come before the scenario is finished is wrong. It can be acceptable if it comes from a character in a realistic way, but otherwise, they should just see the consequences of their choices. Even if it would take a while, that can be accommodated in the storyline: such as “Three months later…”.

Not having a plausible situation is also a miss. The situation which the learners are dealing with, even in a fantastic situation (e.g., on a spaceship), should be a recognizable application of the knowledge to a situation they’re likely to face. Learners quickly recognize implausible situations, and their subsequent effort at making appropriate choices diminishes. Keep it real!

The same holds true with dialog. Having folks speak in perfect sentences, with no quirks, is unusual. Informal language is better for learning anyway, but it’s particularly important that dialog sound natural. Different characters are likely to have different personalities and backgrounds, and their language should reflect that.

The nuances matter in making scenarios that will lead to learning. Some of the elements are learning-specific, such as the right situation for the learner to face. Others are engagement, making that situation seem believable. Integrating both is more effort, but the outcomes are worth it when learners are developing the critical skills the organization needs.

In conclusion, effective scenario-based learning is essential for meaningful skill development. Avoid common mistakes in scenario design, and ensure choices are actionable, feedback is relevant, situations are plausible, and dialogues feel natural. For deeper insights, click here to access our eBook: “Scenario-Based: Learning The Ultimate Asset In Your L&D Toolkit” and unlock the key to effective learning through scenarios.

The post Do’s and Don’ts of Scenario-Based Learning first appeared on The Upside Learning Blog.

If scenarios are a powerful learning solution, how do you go about designing them? As with all learning design, you need appropriate initial information gathering, then aligned design, implementation in appropriate technologies, and evaluation to test and finalize the design. However, there are nuances to scenario design that should be observed.

Analysis

Initially, there should be performance objectives to achieve, that state what the outcome should be. If this hasn’t been done, it has to be part of the initial analysis. That is, you want to identify the problem in the organization and the root cause. If it’s not a training problem, scenarios won’t help. If it is, having a precise definition is crucial. You need to know what learners need to be able to do at the end because that’s what they’ll have to do in the scenario!

There are a few other things needed from the analysis as well that are crucial to scenarios. For one, you need to know what mental models guide performance. Here, it’s for the feedback that comes at the end of the scenario. You’ll also need to know what the full suite of contexts is in which these choices are made.

You also need to know the things learners or performers do wrong on the job. These will make the alternative choices in the scenario! You’ll also need to know the consequences of the right, and wrong, choices. Further, you’ll need to know what these choices lead to. If they’re linked, you’re looking at a branching scenario, at least for the final practice. These are presented as part of the scenario!
Finally, having good stories from your SMEs, about wins and losses, is useful for choosing settings for the scenario. You’ll likely want to document your findings and intentions and get a sign-off before proceeding.

Also, at this stage, specify what the criteria will be for acceptance. What level of user performance do you need (typically stipulated in your performance objectives, c.f. Mager’s formula for good objectives)? Further, what will be an acceptable level of engagement? If you want the best from learners, it’s desirable to make the scenarios engaging: that is, truly embody critical decisions, have compelling settings that learners recognize are important, and flow from beginning to end in a plausible storyline. How will you know this is true? Determine now what will be acceptance criteria.

Specification

Once you’ve got your criteria that the scenario has to address: the right choice, mistakes, consequences, and contexts, you’re ready to plan scenarios. Don’t worry about implementation details yet, focus on the flow.

In general, you want to start with your final practice, which is close to the real performance, and then figure out what additional scenarios you need. You want to think about the appropriate spread of contexts and the steady development of capabilities for the task to be learned. You’ll essentially want to have a map of what the full suite is.

From there, you go into the details of them. What is the starting scenario? Is it mini, linear, or branching? Don’t immediately assume your scenarios have to be real-world settings. If you’re looking for far transfer, that is to a wide variety of contexts, you may find that a fantastic setting (e.g., Wild West, medieval times, outer space, etc.) may facilitate transfer better than an exact replication of the real-world.

Thus, you need a setting for each scenario. Where will they have to make these decisions? What is the minimal set of contexts to provide application in all appropriate settings? Then, how can you make it more dramatic? Don’t just make a deal, save the company. Don’t just help a person, make it the ambassador’s daughter. Try to make the setting more dramatic than real life, because in real life it will really matter!

Don’t forget to ensure that the decision is pitched at the right level for the learner. If it’s early in the sequence, it’s likely simplistic, but as you develop their ability, you need more challenges, until you’re at the level that they’re ready to go perform. So you want to make sure the decisions get more discriminating as you go.

One of the likely elements in your scenarios are other characters. For each scenario, who are the players? What are their distinguishing characteristics? Avoid stereotypes; find plausible background details that explain why they’re how they are. In writing for films, text, or games, it’s generally recommended to put more detail into the character than appears in the experience.

All these elements are largely the same for example scenarios. They need contexts and decisions, but the choice is already made. What they need to do, besides tell a compelling story, is show another setting that assists learners in understanding how the model plays out in practice.
You’ll want to create storyboards for any scenarios that are more than just a mini. When you’ve stipulated what the experience should be, then you can worry about how it will be created.

Implementation

These days, most authoring tools not only support multiple choice questions but even branching scenarios. Some will even let you carry over ‘state’ (the consequences of a choice) to a later place in the decision tree, to allow more complexity.

Thus, you are likely to be able to use scenarios in ways that are already familiar. There are, however, tools (e.g., Twine) that make it easier to represent your structure, and thus minimize problems. However, if you’re doing branching scenarios at scale, you may want to look at the industrial-strength tools (c.f. BranchTrack). Note that these mentions are not recommendations, merely pointers.

Do some tuning, however. Make sure, for instance, that the dialog is natural. Too often, our written language is too formal to begin with, and for dialog, it’s even more important to be natural. Consider having someone review your work or have someone who’s skilled at dialog handle that part of the writing. Similarly, ensure your visual look and feel is not only consistent but also appropriate.

Evaluation

One thing that is mentioned above needs to be made clearer: iterate! That is, whatever you create at first, plan to test and refine. Humans aren’t as predictable as, say, concrete, and as such, anything you do is likely going to have somewhat complex and unpredictable reactions.
Thus, you should be testing to see if you’ve hit the acceptance criteria you stipulated in the analysis phase. Performance in the scenario should be an acceptable path to a successful conclusion. How about the experience, however? That too, is worthy of testing, and certainly will be a factor in whether the scenario is effective. As a capture of a situation, if learners don’t perceive it as plausible, their reactions won’t reflect how they’d perform in a real situation. Therefore, such scenarios aren’t good learning tools.

While you shouldn’t have usability problems, those should be eliminated first. Then, you evaluate the learning effectiveness. Finally, you test engagement. Fortunately, engagement is subjective, so your learners can simply tell you what they think (in ways that minimize their need to dissemble). You don’t need to measure adrenaline in the blood or galvanic skin response.

Really, this shouldn’t be much different from what you normally do for learning, but that focus on mistakes and consequences is doubly important here. Still, it likely will take some practice before creating scenarios becomes a well-established habit.

Scenario design is a potent learning tool. To create effective scenarios, you must first analyze performance objectives, specify acceptance criteria, plan engaging scenarios, implement them efficiently, and continually evaluate and refine them. Mistakes and consequences are crucial in guiding learners. For a deeper understanding of scenario-based learning, download our eBook, “Scenario-Based Learning: The Ultimate Asset in Your L&D Toolkit.” If you have specific questions or would like to discuss scenario-based learning in more detail, we invite you to reach out to us at eLearning@upsidelearning.com. We’re eager to engage with you and support your learning objectives.

The post Considerations for Scenario-Based Learning Design first appeared on The Upside Learning Blog.

A second, associated question, is what scenario structure to use. You’ve seen that there are several, and it pays to know which to use when. So that’s a second goal here.

When to Scenario, or Not

Scenarios are contextualized practice, which is what learning generally requires. While scenarios, by themselves, aren’t a total solution, they’re part of an effective solution.

Learning science tells us that asking learners to use knowledge to make decisions, like those that will be required in the performance situation, is the best way to develop the ability to perform. Retrieval practice, where you are required to access the knowledge and put it into action, is reliably demonstrated to be the best form of learning.

While you can do mentored live performance, e.g., apprenticeships, there are times when this isn’t viable. Such as when the performance is too critical to trust to those still learning. Similarly, individual mentoring doesn’t scale well. However, scenarios are a good stepping stone on the way to such performances. For instance, Microsoft Flight Simulator didn’t start out as a game, it started out as a training tool on the way to allowing learners to pilot real planes!

Thus, scenarios make sense for practice. They can and should be supplemented with mental models to guide the decisions and serve as a basis for feedback. They also should be accompanied by examples, where learners see how the models play out across different contexts. For instance, showing worked examples (examples where the underlying thinking is made explicit), leads to better performance for novices than just providing practice!

What examples do, then, is demonstrate contexts. To do so, it makes sense for it to be a narrative, so they’re scenarios, too. For skills that need to be transferred to different situations, having a suite of mini- or branching-scenarios can be more useful than real practice. By providing a broader set of experiences, you enable learners to abstract and internalize the underlying model, equipping them to perform in situations where you haven’t time to provide all instances.

There are times when learning isn’t the answer. Too often, we use courses when we could use performance support, putting knowledge ‘in the world’, instead of trying to put it in the head. Tools like look-up tables, decision trees, procedural guides, and more, can be more effective than learning. However, when learning is required, particularly at scale, scenario-based learning makes sense.

What Scenario to Use

The other situation to consider is which scenario to use. Given that there are different structures, different situations can call for different approaches. Which to use when? The key is to map the structure of the task to the structure of the scenario. If it’s several steps or can be, you’ll need a branching scenario. Maybe not for all of the learning experience, you can use mini-scenarios as preparation, but at least to close off the learning.

As discussed in the previous chapter, linear scenarios make sense when you’re trying to ensure that each learner faces the same question. You could do that with separate questions, but embodying it in a situation with a story, and playing out the whole process, provides the opportunity to assess the understanding at every stage.

Similarly, there are times when decisions are linked. If you make a mistake in dealing with a customer, can you find a way to get back in their good graces? If you’re working through a process, what happens if you make a mistake? Decisions sometimes travel in packs, and then branching scenarios make sense. Anything that takes a sequence of decisions, such as approving a loan, or overseeing a project, is a plausible candidate.

Of course, you can test the individual decisions alone, or at least first. This is a time when mini-scenarios make sense. They’re easier to develop and deploy than branching scenarios. Not that they’re trivial to write, as experience has let us know. A series of them frequently precedes a branching scenario as steps along the way.

Scenarios are a very powerful tool to achieve learning, and most learning situations will benefit from them. They should be the default, in fact, in most learning designs. There will be times they don’t make sense, but most of the time having learners perform in settings that reflect the tasks they’ll have to perform is the shortest path to mastery.

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Essential Structure

At the core, a scenario is composed of several critical elements: a story setting, a precipitating situation, a decision with several alternative choices of action, and the consequences of those decisions – which can lead to a new situation. Underneath is a world in which the story is set and determines the outcomes of choices made by making the decision.

The story setting is the context. Within the story, some circumstance sets up the need for some choice of action. The correct answer is one option, of course (unless to make a particular point, there are no right answers). That has consequences. There are also wrong answers, with their ‘different’ consequences. One point we’ll address in more depth later is that those alternatives to the right answer shouldn’t be random or silly, but instead should represent reliable ways people go wrong.

This structure, with decision but only one choice and consequences, is also the structure of the example. There are separate nuances, however. The consequences should be explicitly conveyed, and the model should be explicitly mentioned in the decision. If it’s a wrong choice, there may need to be an explicit discussion of why the model didn’t apply in these circumstances. Introductions are different types of examples; they’re not used as a reference about how models work in context, but instead are motivating about why the coming learning is important. This essential structure can vary in distinctive ways, so it’s useful to characterize it in slightly abstract terms. The world, according to scenarios, can be in any valid state of the model – needing a decision, having made a decision – that represents the world. The transition from one state to another can be programmatic, in the case of a simulation, or simply hard-wired in other instantiations. As a consequence, there are several sub-categories of scenarios.

Implementation

First, scenarios can be implemented via simulations driving the decisions. That is, an engine takes learner actions and calculates the change in the underlying model, then presents the outcome to the user for the next decision. There can be an underlying loop keeping the world moving, so you get action and learners have to respond. There’s also a check to see if terminal conditions (e.g., win/lose) have been achieved. Otherwise, the engine just keeps processing user input and determining the new situation to present to the learner.

However, the canonical form is a branching scenario. There are a range of ways these can be implemented, in practice. Similarly, there’s the simplest form, what we term a ‘mini-scenario’. Conceptually, it is worth distinguishing between the various forms, as they serve different learning needs.

For branching scenarios, as stipulated earlier, the rules about how the world works are implicitly communicated through the branches. Thus, the choices don’t trigger a recalculation of states, but instead, the choice is explicitly stated as a link to the subsequent outcome. It’s certainly a lot easier to tie together actions and consequences than to stipulate the underlying model.

There are a variety of forms for different branching scenarios. They can broaden, and narrow at particular spots, or they can be relatively shallow (not many decisions). They’re designed, however, to catch real-world relationships, so for instance, if you make a mistake with a client, you can try apologizing and see if you can get back on track.

A particular form is one I call the linear scenario. Here, no matter what choice you make, you see the consequences, but then somehow, the situation is made right before the next step. So, for instance, a boss can catch the deliverable before it goes to the client. This can be important if every learner needs to face the same problems, for instance for an assessment.

A final type of scenario is what I term a ‘mini-scenario’ (there are other terms for these, as well). Here, it’s only one decision, and you see the consequences of each answer. Really, this is just a better-written multiple-choice question, so you can use your existing tools. However, there is a caveat, you need to make sure there’s different feedback for each wrong answer (but you should be doing that anyway). Don’t forget to have consequences first!

Media

We’ve seen scenarios executed as text adventures, graphic novel formats, audio stories, branching video, and full games. Different media have different strengths, but all are vehicles for dynamic experiences. They can also serve to illustrate examples and introductions.

Visually, images can help establish the story setting, and can convey the consequences as well. Video can provide a richer context, at the risk of more production costs. Branching video requires considerable forethought, but can yield very immersive experiences, with dynamic contexts and vivid consequences.

Text, of course, is visual, but is processed differently. You could do a scenario all in text (think the early text adventure games, such as Colossal Cave), but you can also augment with visuals, as above.

Audio alone is unusual for a scenario, but the possibilities are there. For situations where audio is the key (for example, air traffic control), having audio as at least an element is important. It can be the only channel in some instances, such as the visually impaired.

Tactile isn’t as frequently used but can be critical for learning that requires tactile experience. There are devices that receive and provide haptic feedback, so mimicking the characteristics of a surgeon’s knife or showing motion.

Tradeoffs

The real issue is the tradeoffs in costs and learning. The greater the production values required (e.g., audio and video), the greater the effort and costs. Yet, there is also increasing learning value as you move from mini-scenarios to branching scenarios. There are situations that also justify the cost of a full simulation-driven experience. The trick is knowing which to use when.

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The “What” of Scenario-Based Learning

First, what are scenarios? In my book Engaging Learning, I argued that simulations are just a model of the way a part of the world works. They can be in any valid state, and taken to any other valid state. A motivated and effective learner can engage with the simulation to discover the important relationships. However, that’s not the way to bet.

So, we, as learning designers, put the simulation into an initial state, and ask the learner to take it to a goal state (which we’ve chosen to require them to master the necessary relationships to achieve). I call that a scenario. I go on and suggest that we can tune that into a game. That is, make the scenario engaging enough that people consider playing it to be ‘hard fun’.

In many instances, such games are built upon actual simulation models of how the world works. However, a shortcut to such an implementation is what are called branching scenarios. Here, the model’s relationships aren’t explicit in rules but are instead implicit in the branches. In situation 1, if you choose option A, you go to situation 2. If you choose option B, you go to situation 3, etc. Theoretically, you’re just taking it from one valid state to another, however, it’s not done by rules (as in simulations), but by hard-wiring.

This provides a much easier way to build a learning experience than programming the actual underlying domain model. There aren’t the essentially unlimited replay possibilities that a programmed model would possess, because you can’t program in probabilistic responses, but they’re easy enough to build (relatively speaking) that you can do several, instead.

There’s another role for scenarios. For interactive ones, the learner plays a role in how the scenario plays out. However, we can think about scenarios as stories. Examples, where a model is shown in context, are best communicated as narratives. Even an introduction can use a scenario, where it’s not as explicitly instructional as an example, but communicates the essence and the consequences.

So why would we want to do this?

The “Why” of Scenario-Based Learning

There are several reasons to consider scenario-based learning, but the prime one, in my mind, is because it works. If you design applying what’s known about learning science, it’s clear scenarios provide the right support.

The important point is that you’re being put in a situation that requires applying knowledge to make a decision, e.g. to take an action. From a learning perspective, that’s a contextual practice. As Patti Shank defines scenarios in her book Write Better Multiple-Choice Questions to Assess Learning, they’re “a realistic situation that prompts participation to make decisions using provided details”.

What we do in a scenario is establish a particular circumstance that requires a decision. I’ll stipulate that what will make a difference to your organization is unlikely to be the ability to remember arbitrary information. Instead, I suggest that what will make a meaningful difference is the ability to make better decisions, and that’s what scenarios do! They inherently have you make a choice and experience the consequences thereof. Then, you can provide feedback as well, but the consequences themselves are a powerful form of feedback.

One of the demonstrable results of learning science is that solving contextualized problems leads to better retention and transfer than solving abstract problems. Retention over time until the learning is needed, and transfer to all appropriate (and no inappropriate) situations, are the goals instruction should strive to achieve. Scenarios are problems set in contexts, so they’re natural vehicles for meaningful practice.

What’s happening is that we’re tapping into our natural proclivity for comprehending stories. In a scenario, we establish a setting and choices of actions that learners, by their decisions, continue the story. Our brains are wired to comprehend stories, and stories are context.

This is what works for examples, too. Instructionally, we know that having models that explain how the world works gives us a basis for making decisions about actions to take. However, it assists learners to see those models as they manifest in particular contexts as a basis to support performance once it comes to practice.

That’s the role of an example, but using a narrative, with context, setup, decision, and consequences is a powerful way to leverage our brain’s alignment to stories. Similarly, they work for introductory examples as well. They can communicate the value in high-stakes examples to motivate performance.

Thus, scenarios are, next to mentored live performance, the best practice that you can achieve, and a valuable adjunct as well. Further, mentored live performance has a couple of potential downsides: failure can be costly, as can individual mentoring. Scenarios simulate live performance and provide scripted outcomes and coaching.

Scenario-based learning provides powerful learning for a reasonable cost. As such, we maintain that it’s an important tool in your repertoire of learning solutions. There are many tradeoffs to be explored, of course, hence the subsequent chapters.

In conclusion, scenario-based learning offers a powerful pathway to meaningful skill development and decision-making abilities. To delve deeper into this impactful approach, we invite you to explore our comprehensive eBook, “Scenario-Based Learning: The Ultimate Asset in Your L&D Toolkit.”

Explore the benefits of scenario-based learning – click here to download our eBook and uncover more insights!

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