MODULE
Designing a Coursera Module
A well-designed Module develops a focused part of the knowledge or skill learners need to achieve a Course-level objective. Define what learners should know or be able to do by the end of the Module, then plan the instruction, practice, feedback, and assessment that will help them get there.
A Module at a glance
1–3 learning objectives
per Module
~30 minutes minimum
of learner time
~10 Learning Assets
1 graded assessment
required per Module
Use this section as a reference during your design meetings. Your Learning Experience Designer will guide you through the relevant questions, and you’ll record your decisions in the template as you work together.
Write the Module objectives
Course objectives describe the larger skills learners should develop by the end of the Course. Module objectives break those skills into smaller, achievable steps.
You might ask
Which part of a larger Course objective will this Module help learners develop?
Rather than asking
What topic should this Module cover?
A Module might introduce a concept, guide learners through a process, make an expert's reasoning visible, or give learners an opportunity to apply a skill. Write 1–3 objectives that describe what learners will be able to demonstrate by the end.
Worked example
Writing Module objectives for Identifying and Evaluating Business Data
In Foundations of Data Analytics for Business, one Course objective is:
Course objective
By the end of this Course, learners will be able to determine whether available data is relevant, complete, and suitable for addressing a business question.
The Module develops this larger capability through three focused objectives:
Module objectives
By the end of this Module, learners will be able to:
- Identify the data needed to investigate a business question.
- Evaluate an available dataset for relevance, completeness, and basic quality concerns.
- Document limitations that could affect the proposed analysis.
These objectives give the Module a clear destination. Learners are not simply being introduced to “data quality.” They are learning to make and explain a practical judgment about whether data is fit for a particular purpose.
Plan backward from the learning task
Once the Module objectives are clear, design the Module around how learners will show what they have learned.
What will learners do to show what they’ve learned?
Start with the task learners should be able to complete by the end of the Module. Then work backward to identify what they already know, what they still need to learn, and what practice and feedback will help them succeed.
Use these questions to clarify the learning path:
The final task
What will learners do, create, analyze, evaluate, or decide to show that they have achieved the Module objectives?
What a successful response looks like
What should learners include, do, or decide to complete the task successfully? How will the activity help learners recognize whether they have met the expectations?
What learners already know
What should learners already know or be able to do before they begin?
What learners still need to learn
What new knowledge, steps, or decision-making skills do learners need to develop? Where might they become confused or make mistakes?
How learners will know they are on track
What feedback, answer explanation, model, checklist, or other support will help learners judge their own work?
Worked example
Turning Module objectives into a learning task
In Identifying and Evaluating Business Data, learners are working toward three Module objectives:
- Identify the data needed to investigate a business question.
- Evaluate an available dataset for relevance, completeness, and basic quality concerns.
- Document limitations that could affect the proposed analysis.
To plan the final task, consider what learners would need to do to provide evidence of each objective:
| Module objective | Evidence of learning |
|---|---|
| Identify the data needed | Specify the variables, records, time period, and level of detail needed to investigate a business question |
| Evaluate an available dataset | Compare the available data with those requirements and identify concerns |
| Document limitations | Explain how missing, incomplete, or poor-quality data could affect the proposed analysis |
These actions can be brought together in one meaningful task. Learners could receive a business question and an unfamiliar dataset, inspect the data in relation to the question, and document whether the dataset is fit for the proposed analysis.
For this Module, we will call that task a Data Fitness Review.
The final task
Complete a Data Fitness Review that evaluates whether an available dataset is suitable for addressing a business question and documents concerns that could affect the proposed analysis.
Now work backward from that task:
| Planning question | Decision for this Module |
|---|---|
| What does a successful response look like? | The review identifies the data the question requires, evaluates the dataset’s relevance and completeness, notes basic quality concerns, and explains how any limitations could affect the analysis. |
| What do learners already know? | Learners can recognize common parts of a dataset, including records, fields, and values. |
| What do learners still need to learn? | They need criteria for evaluating data in relation to a particular question and practice explaining why a limitation matters. |
| Where might they struggle? | Learners may focus on whether the data appears clean without noticing that essential variables, time periods, or levels of detail are missing. |
| How will they know they are on track? | A worked example, data-quality checklist, guiding questions, and explanatory feedback will help them compare their reasoning with the evaluation criteria. |
These decisions establish what the Module must prepare learners to do. The next step is to arrange the necessary explanation, modeling, practice, and feedback into a coherent learning path.
Design the learning path
Modules do not need to follow one fixed structure. Some topics require more explanation, while others benefit from repeated practice, multiple examples, or opportunities to make and correct mistakes.
Many effective Modules include some version of this learning arc:
These stages may repeat or overlap. For example, learners may move through explanation, modeling, practice, and feedback several times before completing the final task.
Worked example
Mapping and supporting the learning path
The backward-planning process established that learners will complete a Data Fitness Review. To succeed, they will need to identify the data a business question requires, evaluate an available dataset against those requirements, and explain how its limitations could affect an analysis.
The learning path prepares learners for each part of that task:
Choose Learning Assets intentionally
After mapping the learning path, consider what learners may need at each stage. Then choose the Coursera Learning Asset that best supports that part of the experience.
Begin with the learner’s experience rather than the format. A worked example, for instance, is an instructional strategy that might be delivered through a video or a reading. Practice might take place through an ungraded assessment, Coach Role Play, a programming assignment, an interactive plugin, or a Coursera Lab.
The same asset can serve different purposes. A video might explain a concept, demonstrate a process, or make expert reasoning visible. What matters is how the asset helps learners progress toward the Module objectives.
Learning Assets available in Coursera
| Coursera asset | Especially useful when learners need to… |
|---|---|
Video
Before you plan Video Video can take several forms, and the form affects what learners see and how the video is produced. Review the video examples gallery ↗ and note anything you would like to discuss with your CTL team. Most Coursera videos are planned as in-studio green screen recordings, and self-recording is always an option. Other recording types, such as on-location video or recordings that need special equipment, are usually discussed earlier in project scoping so that budget and timeline can accommodate them. |
Hear a focused explanation, see a process demonstrated, or observe expert reasoning |
| Reading | Study detailed information, review a framework, examine graphics or code, or return to a reference while completing a task |
| Interactive Plugin | Manipulate information, explore a model, use an external tool, or see how choices affect an outcome |
| Coach Dialogue | Explain their reasoning, reflect, make decisions, or receive personalized guidance through an AI-supported conversation |
| Coach Role Play | Practice interpersonal or job-related skills through a realistic interaction with an AI persona |
Assessments
Requirement Assessments Each Module must include at least one graded Assessment. Ungraded Assessments are also encouraged as opportunities for learners to practice, check their understanding, and receive feedback before graded work. |
Check understanding, practice with feedback, or demonstrate achievement of the Module objectives |
| Programming Assignments | Write, modify, debug, or evaluate code and receive feedback on its performance |
| Coursera Labs | Practice with code, data, software, or technical tools in a configured, in-browser environment |
Choose a Learning Asset that fits the learning need
As you consider the options, ask:
- Does the learner need to read, watch, listen, interact, converse, practice, or create?
- Would an explanation, reusable reference, demonstration, guided practice, feedback, or opportunity to demonstrate the objective be most helpful?
- Would this format add meaningful value, or might another asset serve the same purpose?
- What instructions, preparation, or support will learners need to use the asset independently?
- How will the asset connect to what learners encountered before and what they will do next?
You might ask
What does the learner need to do next, and which Coursera Learning Asset might support that need most effectively?
Rather than asking
How many videos and readings should this Module contain?
Worked example
Choosing Learning Assets to support your Module objectives
The learning path for Module 1: Identifying and Evaluating Business Data establishes what learners need to experience: an introduction to the evaluation criteria, a model of expert reasoning, supported practice with feedback, and an opportunity to complete the Data Fitness Review independently.
The next step is to organize those experiences into Lessons and select the Coursera Learning Assets that will support them.
| LEARNING PATH | Learning Assets | LEARNING PURPOSE |
|---|---|---|
| Frame the task |
Video Start With the Business Question |
Introduces a realistic scenario and explains why data must be evaluated in relation to the question it is intended to address |
| Identify the data needed |
Reading From a Business Question to Data Requirements |
Helps learners identify the variables, records, time period, and level of detail a question requires |
| Introduce the evaluation criteria |
Video Is the Data Fit for the Question? |
Explains how relevance, completeness, and basic quality affect whether data can support an analysis |
| Provide a reusable reference |
Reading Data Fitness Criteria and Checklist |
Gives learners criteria and a checklist they can use during the model, practice activity, and assessment |
| Model the process |
Video Think Aloud: Inspecting a Sample Dataset |
Shows an expert comparing a dataset with a business question and documenting concerns |
| Practice with support |
Hands-on activity Practice Data Fitness Review |
Gives learners a second dataset to evaluate using guiding questions and the checklist |
| Prepare learners to communicate their judgment |
Video Documenting Data Limitations |
Models how to explain why a limitation matters and how it could affect an analysis |
| Demonstrate the Module objectives |
Auto-graded quiz Data Fitness Review |
Presents a new business question and dataset and asks learners to identify requirements, evaluate the data, and make supported judgments |
These assets can be organized into a smaller number of Lessons. A Lesson is not a single asset; it is a purposeful segment of the learning path that may bring several assets together.
For example:
- Lesson 1: Identify the data the question requires includes a Video and a Reading.
- Lesson 2: Evaluate whether the data is fit for purpose includes two Videos, a Reading, and a hands-on activity.
- Lesson 3: Complete a Data Fitness Review includes a Video and the auto-graded quiz.
The Module meets Coursera’s asset expectations while keeping every selection connected to the learning path. The goal is not to minimize the number of assets, but to avoid adding assets that do not help learners progress toward the final task.
Complete your Module plan
Use this page to work through your design decisions, then record them in your Coursera Course Design Document. The sample below shows how those decisions might come together for Module 1: Identifying and Evaluating Business Data from Course 1: Foundations of Data Analytics in the example Specialization: Data Analytics for Business Decisions. Use it as a reference as you develop your own plan.
Module title
Best practice
Module title
Give the Module a clear, focused purpose within the Course. A strong title signals what learners will learn, do, or produce—not simply where the Module appears in the sequence. Favor descriptive titles over labels such as Module 2 or Introduction to…
Draft what learners will learn, do, or produce in this Module.
Identifying and Evaluating Business Data
Module description
Best practice
Module description
Orient learners to what they will accomplish and why it matters. Keep the description learner-facing and focused on the Module’s purpose rather than listing every topic or learning asset.
Draft a learner-friendly 2-3 sentence summary of the Module content and learning outcomes.
Learn how to determine whether available data can address a business question. You will identify the data a question requires, evaluate a sample dataset for relevance, completeness, and quality concerns, and document limitations that could affect an analysis.
Module learning objectives
Best practice
Module learning objectives
Break the larger Course objectives into 1–3 smaller learning goals learners can reasonably achieve within this Module. Each objective should describe something learners can demonstrate and help guide the practice and assessment you design.
Begin with observable verbs such as identify, calculate, apply, analyze, interpret, evaluate, create, or recommend.
Write 1-3 actionable, measurable learning objectives that describe what learners will be able to do by the end of the Module.
- Identify the data needed to investigate a business question.
- Evaluate an available dataset for relevance, completeness, and basic quality concerns.
- Document limitations that could affect the proposed analysis.
Estimated learning time
Best practice
Estimated learning time
Estimate the complete learner experience, including videos, readings, activities, practice, and the graded assessment—not just the time spent viewing instructional content. Keep the Module focused enough to feel like a manageable step in a self-paced Course.
Approximately 45 minutes
Graded assessment title
Best practice
Graded assessment title
Use a learner-facing title that reflects the learning being demonstrated, rather than a generic label such as Module 2 Quiz. The assessment title should help learners understand what they are about to do.
Data Fitness Review
Lessons & Learning Assets
For anything created outside this template, please include a link to the final or draft source. This will help your Learning Experience Designer build the content directly from the source. Lessons are optional, so you may leave this column blank or remove it if you're not using lessons.
Aim for a minimum of: 3–6 videos · 1–2 readings · 1–2 activities · 1 auto-graded quiz
| # |
Lesson (optional) Best practice Lesson (optional) Use Lessons when they help organize several learning assets around a meaningful subtopic, task, or stage of learning. A Lesson should make the Module easier to navigate—not add another layer of structure simply for its own sake. Short Modules may not need Lessons at all. Learning assets can appear directly in the Module when that creates a clearer experience for learners. |
Asset Type
Best practice Asset type Choose the learning asset based on what learners need to do at that point in the learning experience, rather than defaulting to video. Different formats support different purposes: Explain or demonstrate → Video Provide reference or step-by-step guidance → Reading Connect learning to experience or reason through a case → Dialogue Try a skill directly → Hands-on Practice, Lab, or interactive activity Check understanding and receive feedback → Practice Quiz Demonstrate achievement → Graded Assessment When appropriate, aim for a rhythm of: Learn → Try → Receive feedback → Apply This often gives learners more opportunities to use what they are learning than placing all instruction first and all assessment at the end. |
Asset Title + Link
Best practice Asset title + link Use specific, learner-facing titles that tell learners what they are about to learn or do. Avoid production labels such as Video 2, Reading 1, or Activity. Clear titles also make the learning path easier to scan in a self-paced Course. Linking the source Link to the most current source material your Learning Experience Designer should use to build the asset. Make sure the source is accessible to your CTL team, and indicate whether it is draft, under review, or final when useful. Whenever possible, link to editable source material rather than a copy created only for review. |
|---|---|---|---|
| 1 | Identify the data the question requires | Video | Start With the Business Question |
| 2 | Identify the data the question requires | Reading | From a Business Question to Data Requirements |
| 3 | Evaluate whether the data is fit for purpose | Video | Is the Data Fit for the Question? |
| 4 | Evaluate whether the data is fit for purpose | Reading | Data Fitness Criteria and Checklist |
| 5 | Evaluate whether the data is fit for purpose | Video | Think Aloud: Inspecting a Sample Dataset |
| 6 | Evaluate whether the data is fit for purpose | Hands-on activity | Practice Data Fitness Review |
| 7 | Complete a Data Fitness Review | Video | Documenting Data Limitations |
| 8 | Complete a Data Fitness Review | Auto-graded quiz | Data Fitness Review |
Additional build notes, dependencies, or accessibility information
Best practice
Additional build notes, dependencies, or accessibility information
Use this space for information the Learning Experience Designer needs that isn’t clear from the source material itself. Note required sequencing, dependencies between assets, setup needs, accessibility considerations, external tools, or special build behavior.
For a self-paced Course, pay particular attention to anything learners may need in order to complete the experience without live instructor support. Consider whether learners will know what to do, what materials or tools they need, and how they can check their progress.
Customer dataset supplied as a spreadsheet. No specialized software required. Checklist needs a screen-reader-friendly version.
Course 1 · Module 1 Graded Quiz
Best practice
Graded quiz
Build the quiz around the Module objectives, rather than testing isolated facts from videos or readings. When the objective calls for application, interpretation, or decision-making, design questions that ask learners to apply, interpret, distinguish, or decide.
Provide useful feedback for both correct and incorrect responses. Feedback can explain why an answer works, clarify a misconception, or point learners toward what they may want to review.
Use the Graded Quiz Import Template in your Design Document when drafting quiz questions. Working in the template helps organize questions, answer options, correct answers, and feedback in a format that supports the Coursera build.
Before writing your questions, please review the Coursera Graded Quiz Template and Guidelines. The quiz should assess the objectives for this module.
Your templates
Your Learning Experience Designer will share your template(s) ahead of your first design meeting. Module planning and the Graded Quiz Import Template live inside the Course Template, with a section for each Module.
See sample Module Template ↗ See sample Quiz Template ↗