Critical Choices And Decisions: Part 2

Choices And The Curious Mind

A curious mind always starts with questions. This two-part article is exploring the choices and decisions we make as L&D professionals as well as the choices and decisions we allow for learners to make. Part 1 covered choices leading to engagement, motivation, and effectiveness.

Part 2: Branching, Simulations, And L&D Strategies

1. How do we handle the complexity that comes with branching as we provide choices to learners?
2. Does branching in narrative provide more engagement at all?
3. How to design branching? What tools can help learning professionals reduce the complexity of design?
4. Simulations are driven by the choices learners make. How do we make sure they’re leading to effective learning and not frustration?
5. Are the decisions and choices we’re making as L&D professionals today working? Or do we need to rethink our strategy?

Part 1 of this article series has shown that providing limited, meaningful choices to learners can increase engagement, motivation, and effective learning. What a choice leads to depends on the design. In reality, a choice has consequences (immediate or delayed) which lead to more choices. To imitate this experience in learning design we use branching.

Choose Your Own Adventure

Have you ever read a “choose your own adventure” (CYOA) book? A CYOA book poses a series of decision points where you can decide how to proceed with the narrative. Each choice then takes you to a new path (branching). As a child, I was intrigued by this experience! As an adult, now I know they were just trying to trick us into reading practice—because of course, you tried every path just to see what happens.

It is not just books that use this form of immersive narrative. You can experience it in a game format through Choose Your Own Adventure: House of Danger or by watching the Netflix movie Black Mirror: Bandersnatch.

Branching Design For Learning

At one point in your professional life, you will most likely have to design scenarios with branching. While the goal is not purely engagement like in the examples above, it is surely a way to get people immersed in a narrative. A branching scenario looks like a standalone multiple-choice question. Except, each choice the learner makes “branches” to another scenario, hence the name.

What’s The Advantage Of Using Branching?

The experience is more lifelike and authentic as we tend to make a series of decisions where each depends on the previous, leading to consequences that determine the final outcome. A well-designed branching scenario allows you to “course-correct” just like in real life.

For example, I worked on a sales call once where we recorded several simulated sales calls with multiple experts. Then we “interpolated” these calls on each other to find patterns and decision points. The result was a full-blown branching simulation where agents could make some mistakes but also course-correct and redeem themselves. However, if they made a critical mistake, the branching ended. In the end, learners could see their whole journey with each decision point. Reflection on the previous decisions is a must!

How Many Decision Points Should We Use?

When you design branching, you need to decide how many branching points (or decision points) you will add. Cathy Moore suggests a minimum of seven decision points in each path [1]. One of the best examples of branching design was created by Cathy a decade ago. If you’ve had the chance to play with Connect with Haji Kamal (no longer available) you will probably agree. Cathy has a blog on the behind-the-scenes branching design [2].

What’s The Challenge With Branching?

The branching design looks like a tree. This branching tree can become complex quickly. For example, let’s assume you have 4 options in each scenario and 7 branching/decision points (which means learners will be making meaningful decisions 7 times). The total amount of scenarios you would need to create is 4 (options) ^ 7 (branching points) = 16,384. Good luck building out 16,384 different outcomes! And imagine your SME or stakeholders reviewing those paths! Handling the review cycle itself can be complex because if you change one option anywhere, you have to make sure there is no ripple effect on other branches.

How To Handle The Branching Complexity?

Fortunately, there are ways to make this process less complex. The point of branching scenarios is to provide a more authentic challenge but also a safe place to practice. When a series of decisions can lead to a path where there is no return or a vital mistake is made, it is better to stop the branching and provide immediate feedback. Therefore, you don’t need “true” branching all the time. Some branches will be shorter than others.

True Branching Vs. Shallow Branching

What’s the alternative to true branching (where every choice opens a new path)? “Shallow” branching can be defined as a “perceived” new path when instead several options lead to the same path. In this case, we often use something called weighted error. Imagine you start with zero points and every decision you make adds or detracts from your points. Some decisions can “cost” you more than others. In other words, we weigh the criticality of the decision. When the learner’s points reach the minimum threshold, the course stops. You can decide if learners should be able to step back to course-correct or if you would place them back to an earlier main decision point.

How To Explore Different Branching Structures?

A branching game is one of the best ways to explore a well-designed experience. Remember, the learner will not know the branching structure (unless you reveal it to them as part of the learning process). It is all about the perceived freedom of choice. Here are some examples for you:

• Time cave
  A heavily branching sequence. All choices are of roughly equal significance; there is little or no remerging, and therefore no need for state-tracking. There are many, many endings.
• Branch and bottleneck
  The game branches, but the branches regularly rejoin, usually around events that are common to all versions of the story. To avoid obliterating the effect of past choices, branch and bottleneck structures almost always rely on heavy use of state-tracking (if a game doesn’t do this, chances are you are dealing with a gauntlet).
• Quest
  The quest structure forms distinct branches, though they tend to rejoin to reach a relatively small number of winning endings (often only one). The elements of these branches have a modular structure. Small, tightly grouped clusters of nodes allow many ways to approach a single situation, with lots of interconnection within each cluster and relatively little outside it.

For more details on each branching pattern type, visit the blog Standard Patterns in Choice-Based Games [3]. Christy Tucker also has a great blog on managing the complexity of branching scenarios for learning [4].

Complexity Vs. Depth

A quick note on complexity: learning designers not familiar with game design sometimes make the mistake of mixing up complexity and depth.

• Depth is “the number of emergent, experientially different possibilities or meaningful choices that come out of one ruleset.” Games with high depth are still strategically interesting and fun even after you have mastered the game’s rules.
• Complexity is how difficult it is for the player to understand the rules and their implications. If the game requires the player to track multiple rules at once, it makes it harder for the player to appreciate the depth of the game.

A game can be low-complexity but high-depth like Go, because you learn the rules quickly, yet it has so much depth that every single game is a new experience. You will never be able to go through all combinations of the game. If you’re interested in more examples from everyday applications, check out the article “Depth vs. Complexity in Game Design“ [5].

What Tools To Use For Branching?

No matter what your ultimate learning authoring tool is, branching design requires quick prototypes and iterations. One of the best (and free) tools I can recommend is Twine. Twine is an open source, interactive tool that handles the complexity of branching design. You can use Twine to prototype the paths and get approval on exact wording before you implement it in your authoring tool. Take a look at Christy Tucker’s example [4], or one of my previous articles here on elearningindustry.com about interactive storytelling [6].

Is Providing Branching Choices Leading To More Engagement?

Intuitively, one might think it is obvious that a branching experience is more immersive and therefore more engaging than a linear narrative. First, let’s clarify that engagement doesn’t mean learning. When learning designers add extraneous “engagement” to content (funny pictures, jokes, game elements) that are not supporting the key learning objectives, the course might be entertaining but not effective. I’ve seen many videos (advertised as microlearning) where you passively watch something without making any decisions or reflecting on anything. The videos might get great smile sheets reviews, and yet make no impact on the job.

Therefore, when we talk about engagement in the context of learning, we mean extended focus on meaningful decisions and reflections on consequences. In other words, both cognitive and affective engagement.

Games are an obvious place to look at research on branching narratives. A research paper presented at the International Conference on Human-Computer Interaction explored the hypothesis that “Branching Narrative Feedback in an RPG Leads to Increased Enjoyment and Engagement” [7]:

The degree of user engagement within an interactive narrative lies largely with the player’s perceived control over the character: the greater the sense of control over the character, the greater the sense of presence—and freedom.

The paper has found a significant correlation between branching narrative feedback and increased engagement, proving the hypothesis right.

Simulations

There’s nothing closer to authentic situations than simulations, providing a safe place to practice simulated real-world tasks. In fact, every single learning experience should be a simulation of reality. A simulation is not the same as recreating the authentic environment. That would be too expensive and not scalable. Think of a simulation as a magnifying glass over a simplified imitation of reality. You identify the challenging decision points and magnify them for the learner to explore, experiment, reflect, and apply their knowledge and skills in a safe place. That is the essence of experiential learning.

Workplace Simulations: Experiential Learning

Workplace simulations are complex problem-solving scenarios where participants must make meaningful choices to find an acceptable solution. The reason we say “acceptable” is because there might be not one single solution but rather multiple solutions with trade-offs. A simulation is a controlled environment where variables can be adjusted.

In theoretical simulations all elements of the problem are present, so you can practice decision-making with all the information you need. In everyday workplace situations, however, variables are often missing, priorities are not set, you have unclear goals, etc. Therefore, it is critical to establish acceptance criteria that you check your solution against. The question will not be whether a solution is correct or wrong, but rather how correct and how wrong it is against the acceptance criteria.

Acceptance Criteria

Onboarding new hires is a good example. If you’re teaching new hires how the company works “in theory” they might have a hard time with gaining speed to proficiency, because reality is different on the job. You can often observe this problem with managers whose first sentence to the new hire graduate is “Forget what they trained you on! Let me tell you how it really works!” Use the acceptance criteria as the evaluation starting point for your design and work backward from there.

Simulations Require Scaffolding

A meta-analysis with 145 empirical studies has found that successful simulations require different scaffolding depending on the participant’s prior knowledge and skills [8].

Findings were robust across different higher education domains (e.g., medical and teacher education, management). We conclude that (1) simulations are among the most effective means to facilitate learning of complex skills across domains and (2) different scaffolding types can facilitate simulation-based learning during different phases of the development of knowledge and skills.

How Do You Incorporate Scaffoldings In Your Design?

To provide the right scaffolding you need to know about the learner’s prior knowledge and skills. You can design a pre-assessment to determine the baseline. You can give participants a choice (although people generally either underestimate or overestimate their skills level). A pre-assessment could recommend the approach or mandate one path. You can also provide the ability to switch, or give an example in the first round and let the participant decide. When you face decisions like that I always recommend a pilot and A/B testing.

Immersive Simulations

With Virtual Reality (VR) technology, you can take the branching, immersive simulations to the next level. Imagine your original text-based multiple-choice scenario about a difficult conversation taking place in 3D with a person in front of you. Now you can see visual clues, non-verbal signals.

Not only that! Why stop at multiple-choice? Let’s say you get four options to choose from but they’re only guidance for you on how to actually respond. You must put it in your own words and deliver the message.

I built a program for salespeople once where they practiced the pitch online through text-based scenarios. When I asked them to give me the pitch as if I were a customer, the difference was night and day. Even the best salespeople needed a couple of rounds to make it sound like they meant it. Knowing the theory is not the same as applying it on the job.

Perspective: Role Switching

What if the system could switch perspectives for you? Let’s say after delivering your message as a manager, the system would switch roles. Now you’re sitting there as an employee, facing your previous “you” as the manager, and yes, you will listen to yourself delivering the message you previously recorded. Saying something vs. hearing yourself saying it…again, night and day. Try it! You don’t need VR. Record a short presentation on video and watch yourself. It is eye-opening.

This VR experience is not the future of learning, by the way. It’s the now of learning. Check out “An experimental study of a Virtual Reality counselling paradigm using embodied self-dialogue” [9].

Motivational Theories

Part 1 and 2 of this series have explored engagement and motivation through choices. We can’t conclude this exploration without mentioning some well-known motivational theories.

The self-determination theory (SDT) focuses on people’s intrinsic motivation. According to SDT, people need the following to achieve psychological growth [10]:

• Autonomy
  People need to feel in control of their own behaviors and goals. This sense of being able to take direct action that will result in real change plays a major part in helping people feel self-determined.
• Competence
  People need to gain mastery of tasks and learn different skills. When people feel that they have the skills needed for success, they are more likely to take actions that will help them achieve their goals.
• Connection or relatedness
  People need to experience a sense of belonging and attachment to other people.

How Can SDT Inform Our Learning Design Decisions?

We can’t design or deliver learning. Learning happens in someone’s brain. What we should focus on is designing the best conditions for learning to happen and supporting application or learning transfer on the job. We must provide relevant learning experiences. That is, relevant to an individual’s current role, knowledge, and skills level. Choice-driven learning such as branching and simulations can stimulate both autonomy and competence for learners. Supporting the social aspect of learning can increase the feeling of connectedness and relatedness. After all, we are all individual learners but “social workers.”

Keller’s ARCS Model

Similar to SDT, Keller’s ARCS model includes critical elements to incorporate in our design [11]:

• Attention
• Relevance
• Confidence
• Satisfaction

Again, relevance is a key factor in effective learning design when the evaluation of success is about the application on the job and not just the completion of courses. Like SDT’s mastery component, confidence is about the evidence of growing skills and increased ability to do our job better, faster, easier, etc.

Final Thoughts: L&D Strategy Choices/Decisions

Let’s step back from the multiple-choice, branching, and even course design levels to ask the general question: how is L&D doing? Are customers satisfied with the results L&D is delivering?

The answer seems to be “not.” Consider the following large-scale survey results [12]:

• 75% of 1500 managers surveyed from across 50 organizations were dissatisfied with their company’s Learning and Development (L&D) function.
• 70% of employees report that they don’t have mastery of the skills needed to do their jobs.
• Only 12% of employees apply new skills learned in L&D programs to their jobs.
• Only 25% of respondents to a recent McKinsey survey believe that training measurably improved performance.

Time To Evolve?

There is a lot to do to improve. Maybe it’s time to revisit our choices and decisions along with all the biases that can potentially hinder our evolution as learning professionals because being wrong about something is okay. What’s not okay is not evolving. Not evolving as an organization, as a profession, or as an individual is not a choice. Engagement and motivation are effective tools in our tool kit but only if they drive the right behavior on the job, and not just entertain our audience to harvest cookie-induced smartsheets five stars. What’s your next move?

References:

[1] Branching Scenarios: How many decision points?

[2] Scenario based training example: Connect with Haji Kamal

[3] Standard Patterns in Choice-Based Games

[4] Managing the Complexity of Branching Scenarios

[5] Depth vs. Complexity in Game Design

[6] Design Secrets Of An Interactive Storytelling Challenge

[7] Narrative Control and Player Experience in Role Playing Games: Decision Points and Branching Narrative Feedback

[8] Simulation-Based Learning in Higher Education: A Meta-Analysis

[9] An experimental study of a virtual reality counselling paradigm using embodied self-dialogue

[10] Self-Determination Theory and Motivation

[11] ARCS Model Of Motivational Design In E-Learning (John Keller)

[12] Where Companies Go Wrong with Learning and Development

Additional Sources:

Keller, J., and K. Suzuki. 2004. Learner motivation and E-learning design: a multinationally validated process. Journal of Educational Media 29 (3): 229–239.

Miltiadou, M., and W. C. Savenye. 2003. Applying social cognitive constructs of motivation to enhance student success in online distance education. AACE Journal 11 (1): 78–95.

Reeve, J., and H. Jang. 2006. What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology 98 (1): 209.

Ryan, R. M., and E. L. Deci. 2017. Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.

Riedl, M., and R. M. Young. 2006. From linear story generation to branching story graphs. IEEE Computer Graphics and Applications: 23–31.