Level 3 - Lesson 6: Branching the Narrative

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Level 3 - Lesson 7: Evaluation and Revision

Lesson information:

As you watch the video, look out for these learning points:

The 3x3 branching approach helps to keep branching clean and simple
Suboptimal paths should not exercise more skills than are available in the best path
When flowing separate branches into a single decision node, ensure the progression makes sense narratively
Pair variables and conditionals to direct learners on a specific path based on their responses

Branching in Interactive Learning Conversations involves creating multiple decision nodes with various response options.

→ One structure to achieve this is the "Three by Three" approach

which consists of three decision options in each node, leading to three distinct dialogue responses that then circle back to the same decision node.

To create effective branching, realistic learner mistakes should be included, ensuring that these mistakes don't allow the learner to exercise more skills than available in the best path.

The dialogue should respond realistically to the learner's choices and each dialogue should logically link to the next decision node.

Variables can be used to track the learner's performance, such as the number of bad choices made.
Based on a certain threshold, a conditional can be created that gives the learner consequences for their actions.

This concept, known as a "fail out," adds stakes and a sense of urgency to the learning experience, making the learner's choices more impactful.

‣

Video Transcript

Let's talk a little bit about branching. So I have a pre-existing flow open here in co-pilot designer. And I wanted to talk to you about what the process of going from a linear best path to this kind of full branching conversation can look like.
The structure that we have here, I wanna zoom out a little bit. We call this a three by three.
So it's a little hard to see, but we call it this because you have your three decisions in each decision node, three options in each decision node that lead to three different dialogue responses that then all circle back to the same decision node afterwards.
This is definitely not the only way to branch, but especially if you're new to interactive learning conversations, it can be a nice way to keep your flows relatively tight, simple, and not end up with a spaghetti monster.
The real trick to that is first off, starting with your decision node, make sure that you have a learner mistakes that are both realistic.
Make sure that the mistakes that you're representing both, you know, negative and somewhat more neutral choices. Make sure that they don't allow the learner to exercise more skills than are available to them in this best path, which for this example, is up here at the top.
Once you have your realistic decision options, you want your dialogue to obviously respond in a realistic way to what the learner just said.
The key part is to make sure that each of these dialogues can realistically link to this next decision node.
This can be a little tricky sometimes. I find it really helpful to just either in my head or sometimes even out loud if there's no one around read the, the virtual human line that goes into the decision just to make sure it logically flows between each statement.
Any of these statements could equally make sense right after this dialogue by the virtual human. So for this example, you're gonna see up here, this one.
Each dialogue happens to end on the exact same sentence. That's one way to do it. It is not the only way to do it.
There's a lot of little conversational kind of tricks that you can pull. Have the characters sort of go on a longer rant on one and then have it kind of end in a question that conveniently the learner could answer with the decision node.
That kind of thing can bring you back to the start. And then this is kind of your very basic three by three.
There are a little little easy things you can do to modify this. So let's say for example, you throughout this conversation created a variable that tracked the learner's performance.
Let's call it, we're gonna declare our variable over here and we're gonna call it bad choices. All right? So right now you have zero bad choices.
Let's say if the learner over here hit this worst path, we would add bad choices plus one. I'll go ahead and connect it here.
We could go through and repeat that along the best path for as long as we needed to. But at a certain threshold, we could create a conditional that would give the learner some consequences for the totality of their actions.
Let's say we have a conditional if okay, there if bad choices greater than zero. So if you have any bad choices here, then you will go to this dialogue and otherwise you're gonna get to continue on.
At this point, if you've made a bad choice, we could have Tanika say you know, however my office and we could end the conversation prematurely.
This is something we call a fail out, and those we really like using them because it kind of creates more stakes, more of a sense of urgency, and it makes the learner's choices have a bit more of an impact.
Realistically, if you go through the conversation and you chose every single wrong answer, you would have a different end to the conversation than if you chose every.

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Level 3 - Lesson 6: Branching the Narrative

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Level 3 - Lesson 7: Evaluation and Revision

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Lesson information:

As you watch the video, look out for these learning points:

The 3x3 branching approach helps to keep branching clean and simple
Suboptimal paths should not exercise more skills than are available in the best path
When flowing separate branches into a single decision node, ensure the progression makes sense narratively
Pair variables and conditionals to direct learners on a specific path based on their responses

Branching in Interactive Learning Conversations involves creating multiple decision nodes with various response options.

→ One structure to achieve this is the "Three by Three" approach

which consists of three decision options in each node, leading to three distinct dialogue responses that then circle back to the same decision node.

To create effective branching, realistic learner mistakes should be included, ensuring that these mistakes don't allow the learner to exercise more skills than available in the best path.

The dialogue should respond realistically to the learner's choices and each dialogue should logically link to the next decision node.

Variables can be used to track the learner's performance, such as the number of bad choices made.
Based on a certain threshold, a conditional can be created that gives the learner consequences for their actions.

This concept, known as a "fail out," adds stakes and a sense of urgency to the learning experience, making the learner's choices more impactful.

‣

Video Transcript

Let's talk a little bit about branching. So I have a pre-existing flow open here in co-pilot designer. And I wanted to talk to you about what the process of going from a linear best path to this kind of full branching conversation can look like.
The structure that we have here, I wanna zoom out a little bit. We call this a three by three.
So it's a little hard to see, but we call it this because you have your three decisions in each decision node, three options in each decision node that lead to three different dialogue responses that then all circle back to the same decision node afterwards.
This is definitely not the only way to branch, but especially if you're new to interactive learning conversations, it can be a nice way to keep your flows relatively tight, simple, and not end up with a spaghetti monster.
The real trick to that is first off, starting with your decision node, make sure that you have a learner mistakes that are both realistic.
Make sure that the mistakes that you're representing both, you know, negative and somewhat more neutral choices. Make sure that they don't allow the learner to exercise more skills than are available to them in this best path, which for this example, is up here at the top.
Once you have your realistic decision options, you want your dialogue to obviously respond in a realistic way to what the learner just said.
The key part is to make sure that each of these dialogues can realistically link to this next decision node.
This can be a little tricky sometimes. I find it really helpful to just either in my head or sometimes even out loud if there's no one around read the, the virtual human line that goes into the decision just to make sure it logically flows between each statement.
Any of these statements could equally make sense right after this dialogue by the virtual human. So for this example, you're gonna see up here, this one.
Each dialogue happens to end on the exact same sentence. That's one way to do it. It is not the only way to do it.
There's a lot of little conversational kind of tricks that you can pull. Have the characters sort of go on a longer rant on one and then have it kind of end in a question that conveniently the learner could answer with the decision node.
That kind of thing can bring you back to the start. And then this is kind of your very basic three by three.
There are a little little easy things you can do to modify this. So let's say for example, you throughout this conversation created a variable that tracked the learner's performance.
Let's call it, we're gonna declare our variable over here and we're gonna call it bad choices. All right? So right now you have zero bad choices.
Let's say if the learner over here hit this worst path, we would add bad choices plus one. I'll go ahead and connect it here.
We could go through and repeat that along the best path for as long as we needed to. But at a certain threshold, we could create a conditional that would give the learner some consequences for the totality of their actions.
Let's say we have a conditional if okay, there if bad choices greater than zero. So if you have any bad choices here, then you will go to this dialogue and otherwise you're gonna get to continue on.
At this point, if you've made a bad choice, we could have Tanika say you know, however my office and we could end the conversation prematurely.
This is something we call a fail out, and those we really like using them because it kind of creates more stakes, more of a sense of urgency, and it makes the learner's choices have a bit more of an impact.
Realistically, if you go through the conversation and you chose every single wrong answer, you would have a different end to the conversation than if you chose every.

✅

Mark Actions as ‘Completed’ in the Actions Checklist

Download any supporting Resources & Documents:

📘

Mark this Lesson as ‘Completed’ in the Progress Tracker

Level 3 - Lesson 6: Branching the Narrative

Completed

…and then go to the next Lesson! 💪

Level 3 - Lesson 7: Evaluation and Revision

Completed