Exercise Guidance.md

What are Exercises?

Exercises are self-assessed, hands-on practice activities that allow learners to apply one or more skills taught in a lesson. They help learners build confidence, strengthen understanding, and prepare for real-world professional tasks.

Why are Exercises Important?

Exercises provide structured, focused practice that strengthens the job-ready skills taught in the lesson. They help learners deepen their understanding through active application, build independent thinking skills, and prepare for the final project in the course.

Exercise Design

Exercises are designed to give learners focused practice on a specific lesson skill and reflect how practitioners apply that skill in real-world contexts. High-quality exercises:

• Are tightly aligned to a clearly defined target skill.
• Use scenarios or contexts that improve engagement while remaining universally accessible. See Real-World Content Guidelines and Accessibility Standards for more information.
• Require learners to work at the mid to high levels of Bloom’s Taxonomy: applying, analyzing, evaluating, or creating.
• Include only the scaffolding necessary to keep the focus on the intended skill.

✅ Do This

• Focus on one clearly defined skill, e.g, ”Analyze a dataset using descriptive statistics.”
• Use a simple, realistic scenario, e.g, ”You’ve been asked to validate incoming data before it’s loaded into the pipeline.”
• Require mid-to-high cognitive challenge, e.g, ”Evaluate which model performs best and justify your choice.”

❌ Don’t Do This

• Use vague or overly broad skills, e.g, ”Work with data.”
• Include distracting or inaccessible scenarios, e.g, ”Pretend you’re organizing a unicorn festival…”
• Reduce the task to low-level recall, e.g, ”Copy this code and run it as-is.”
• Include instructional design details, e.g., separate sections labeled ”Objectives” and “Prerequisites.”

Exercise Instructions

Exercise instructions provide learners with all the necessary information to complete the task successfully, while still requiring them to think independently and solve problems. Effective instructions:

• Clearly state the goal or outcome of the exercise.
• List all requirements needed to complete the task.
• Avoid detailed, step-by-step directions that remove opportunities for decision-making.
• Use clear, direct language that avoids ambiguity.
• Reference any starter code, files, or tools the learner will use.

✅ Do This

• State what the learner will produce, e.g., ”Create a function that returns the top three keywords.”
• List clear requirements, e.g., ”Your solution must run without errors and handle empty input.”
• Reference resources, e.g., ”Use the starter file provided in the workspace.”

❌ Don’t Do This

• Write step-by-step recipes, e.g., ”First click this, then type this, then run this exact command…”
• Add hidden requirements not listed elsewhere, e.g., “Your solution should also have a CLI mode.”
• Use ambiguous phrasing, e.g., ”Make sure your analysis looks good.”
• Create a scenario that is too complex, e.g., a scenario that requires multiple paragraphs to explain.
• Set a scenario that is juvenile, silly, or not appropriate for a global audience, e.g., ”Pretend you’re organizing a unicorn festival for a group of magical forest creatures...”, “Create a database to track the inventory and vintage of a wine cellar”, “Build an algorithm for a dating app that matches users based on their relationship goals”

Starter and Solution Code for Coding Exercises

The starter and solution code must clearly communicate the task and demonstrate strong coding practices.

• Starter code uses TODO comments to indicate required work.
• Solution code replaces TODO comments with clear, descriptive comments.
• Example code follows the style, structure, and conventions learners are expected to use.

Example (Starter Code)

class Agent:
    #TODO: Create a constructor with required parameters
    def __init__(self):
        #TODO: Instantiate the client
        self.agent = OpenAI()

    #TODO: Create the logic for an invoke() method
    def invoke(self, message: str) -> str:
        return

Example (Solution Code)

class Agent:
    # Initiate the agent
    def __init__(self):
        # Instantiate the client
        self.agent = OpenAI()

    # Invoke the client
    def invoke(self, message: str) -> str:

        return

Solutions

Solutions help learners verify their work, understand the correct approach, and learn from mistakes. In most cases, the solution page will include a video, text, and any relevant code or artifact examples.

Solution Video

A concise video walkthrough is provided for many exercises.

• The video follows the key steps outlined in the exercise instructions and is focused on the skills taught in the module.
• It may include common learner mistakes.
• If the explanation exceeds 7 minutes, it is broken into shorter videos at logical points.

Text Solution

Each video is paired with a step-by-step written solution.

• The solution summarizes the solution and is focused on the skills taught in the module.
• Key code snippets are included in the text, either in line or as separate code blocks.
• Common learner mistakes may be included in a Common Mistakes to Avoid section, even if they are not discussed in the video.

Solution Code/Artifacts

A complete author’s solution should be presented to help the learner review their work.

• For workspace exercises: the full solution should be available in a workspace on the page. Learners should be directed to the solution at the end of the solution summary.
• For local coding workspaces: a link to a GitHub repo containing the solution.
• For non-technical exercises: a full text or downloadable solution.

Exercise Titles

Exercise titles should be descriptive and help learners remember what the exercise covers. Do not number exercises! Numbering conflicts with the classroom UI and our modular build strategy.