SLEAR Series Part 2: The SLEAR Framework
SLEAR Series Part 2: The SLEAR Framework
- Learnmonade
- 5 Minute Read
- AI Case Studies
S: Set the Scene
First, we set the scene.
We cultivate the learning context before proceeding with the design process. This step is the foundation, laying the groundwork for what follows.
In our case, we set the scene as follows:
Topic
How to Have a Difficult Conversation
Key Concepts
- Effective communication strategies
- Conflict resolution techniques
Audience
- Working professionals
- People seeking to improve their communication skills
Mode of Learning
Self-Paced
Duration
Microlearning
L: Learning Objectives
Next, we will set the learning objectives.
To design an impactful learning experience, it is important to have clear learning outcomes. We can decide if we ultimately want the content to simply scratch the surface, or if we want the knowledge to be transferable and invoke a deeper understanding with our learners.
These are our original content point:
Four steps for navigating difficult conversations and producing positive outcomes.
Let’s prompt ChatGPT to come up with 3 objectives for each tier and see what it comes up with.
Based on the topic "How to Have a Difficult Conversation" and key concepts "Effective communication strategies and Conflict resolution techniques". Propose three surface learning outcomes in one short sentence with no more than 280 characters.
Out of the three objectives, we picked out two that we thought applied the best to our mini-course. Click through the tabs to find out what they are.
Surface Learning Objectives
- Participants will demonstrate improved communication skills by applying active listening techniques and selecting appropriate language and tone in simulated difficult conversations.
- Learners will identify and apply conflict resolution models to real-life scenarios, fostering constructive dialogue and resolution in challenging conversations.
Deep Learning Objectives
- Participants will critically analyze personal communication patterns, integrating effective strategies for empathy and active listening into their communication repertoire during difficult conversations.
- Learners will synthesize conflict resolution theories, demonstrating the ability to adapt and apply them contextually for optimal resolution in diverse and complex situations.
Transfer Learning Objectives
- Participants will transfer acquired communication skills to real-world scenarios, effectively engaging in and resolving difficult conversations in personal and professional settings.
- Learners will apply conflict resolution techniques learned to diverse situations, becoming adept at fostering positive outcomes in a variety of challenging interactions.
As a side note, you can prompt ChatGPT to make the learning objectives sound more engaging, but we stuck with our current objectives, as they sound engaging and echo the same message as the generated ones.
E: Evaluate
Let’s move on to evaluate the effectiveness of the learning objectives.
To ensure a successful learning objective, we need to measure it through an assessment or achievement criteria, so let’s prompt ChatGPT on what would be effective for this case.
Based on the following learning outcome(s), what kind of evidence can inform us whether the learning outcomes have been achieved?
a) Surface learning outcomes
We settled for the surface learning outcomes and came up with some criteria to measure the courses’ effectiveness.
Participants will demonstrate improved communication skills by applying active listening techniques and selecting appropriate language and tone in simulated difficult conversations.
- Video recordings of role-playing exercises where participants engage in simulated difficult conversations.
- Transcripts or reflections on these simulated conversations, showing the use of active listening and appropriate language and tone.
Learners will identify and apply conflict resolution models to real-life scenarios, fostering constructive dialogue and resolution in challenging conversations.
- Written analyses of case studies or real-life scenarios, demonstrating the application of conflict resolution models.
- Role-playing assessments in which learners resolve actual conflicts, showcasing the use of learned models.
b) Further evaluation
Let’s further evaluate and prompt ChatGPT for 3 suggestions to ensure the successful attainment of the desired outcomes. We’ll pick one from each that we found feasible.
Applying active listening techniques
Role-Playing Exercises: Integrate regular role-playing exercises into the learning process, covering diverse difficult conversation scenarios. Encourage participants to reflect on their performance and share insights with peers.
Apply conflict resolution models to real-life scenarios
Real-Life Case Studies: Utilize real-life case studies that closely align with the participants’ personal or professional contexts. Encourage learners to apply conflict resolution models to these cases, fostering practical and relevant skills.
In the end, we decided to test the course takers by adding a conversation simulation towards the end to gauge their responses and provide them with feedback based on their responses.
A: Activity
Now we move on to developing the learning activity for our course.
In this phase, you should choose an instructional design method to create the activity. The main question is,’ What activity types are the most effective for achieving the course learning outcomes?’
There are plenty of instructional design methods you can use, such as:
- Directed Learning
- Scenario-Based Learning
- Problem-Based Learning
- Project-Based Learning
a) Directed Learning
We prompted ChatGPT the following:
“Create a directed learning task that helps adults acquire the learning outcomes above. Ensure that the task includes clear instructions, and resources that can support the outcome achievement.”
Click on the image to view the activities ChatGPT recommended. As it was too long, we put it in a PPT format.
b) Scenario-Based Learning
We prompted ChatGPT the following:
“Design a scenario-based learning activity for adults. Craft a real-life scenario that immerses learners in a practical situation. Specify the challenge or problem they should address and outline how the scenario will lead to learning.”
Click on the image to view the activities ChatGPT recommended. As it was too long, we put it in a PPT format.
c) Problem-Based Learning
We prompted ChatGPT the following:
“Develop a problem-based learning task that challenges adults to solve a complex, real-world problem. Describe the problem, provide resources, and outline the steps learners should take to analyze and address the issue.“
Click on the image to view the activities ChatGPT recommended. As it was too long, we put it in a PPT format.
d) Project-Based Learning
We prompted ChatGPT the following:
“Create a project-based learning project for adults that encourages them to work on a meaningful, long-term project. Define the project’s scope, objectives, milestones, and assessment criteria to guide their progress.”
Click on the image to view the activities ChatGPT recommended. As it was too long, we put it in a PPT format.
e) Continuing a Conversation Simulation
Now while all of these suggestions are great, and can incredibly benefit longer courses, or training initiatives, we are simply seeking to revamp our mini-course, so we can refine the prompt further to receive a focused suggestion.
We want to stick with the conversation simulation, so let’s prompt ChatGPT by giving it some more information. Let’s feed it with a previous conversation that already exists in our course, and ask ChatGPT to expand on it.
This is the prompt we input.
“This is a previous conversation simulation in our course. Based on the simulation, please expand on this and create a branched map in full.”
[insert conversation with feedback and choices]
ChatGPT was able to respond with a detailed map with clear paths and a branched scenario for us to choose from, as well as feedback. This is a snippet of the map.
Conclusion
ChatGPT did a lot of the heavy lifting for us, and made our revamp process even better. Now, let’s move on to the last blog in this series detailing the AI tools we used to revamp our course as part of the R: Resources phase in our SLEAR framework.
Check out the previous blog here.
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