Breaking down problems: user stories & structured data
Technologies · Year 7
Name: ______________________Date: ____________
Before building a digital solution, it helps to clearly define the problem — breaking a complex problem down into smaller, more manageable parts (decomposition). A user story is a simple way of describing a feature from a user's perspective, often in the format "As a [type of user], I want [goal], so that [reason]" — helping designers understand what's actually needed and why. Structured data organises information into a consistent format (like rows and columns, or objects with specific attributes), which makes it easier to store, search, and use — for example, modelling a "student" as an object with attributes like name, year level and attendance.
Example
A user story for a school app might read: "As a student, I want to see my timetable on my phone, so that I don't forget which class I have next." This clearly captures who needs it, what they need, and why — guiding the design toward the actual need.
Key terms
Decomposition:
Breaking a complex problem down into smaller, more manageable parts.
User story:
A simple description of a feature from a user's perspective, capturing who, what and why.
Structured data:
Information organised into a consistent, predictable format, like rows/columns or objects with attributes.
Questions
1. Decomposition means:
Combining problems into one
Breaking a complex problem into smaller parts
Ignoring the problem
Making a problem more complex
2. A user story describes a feature from:
The developer's perspective only
A user's perspective
No perspective at all
A random viewpoint
3. A user story typically follows the format:
A random sentence
"As a [user], I want [goal], so that [reason]"
Only technical jargon
A single word
4. Structured data is:
Random, disorganised information
Information organised into a consistent, predictable format
Never used by computers
Always unstructured
5. An object modelling a "student" might have attributes like:
No attributes at all
Name, year level and attendance
Only random numbers
Nothing related to students
6. Breaking down a problem helps you:
Make it more confusing
Manage and solve it more effectively
Ignore the problem entirely
Avoid understanding it
7. Rows and columns are a common way to organise:
Random unrelated data
Structured data
Only images
Only sound
8. A user story "As a student, I want to see my timetable on my phone, so that I don't forget my next class" captures:
Nothing useful
Who needs it, what they need, and why
Only the developer's opinion
A random unrelated idea
9. Why might decomposing a large problem into smaller parts make it easier to solve?
It does not help at all
Smaller, well-defined parts are often more manageable to design and solve individually
Decomposition always makes problems harder
Large problems should never be broken down
10. Modelling real-world things (like a "student") as structured data with specific attributes helps:
Make data harder to use
Organise and process information about them consistently
Remove all useful information
Confuse a computer system
11. Which is an example of well-structured data?
A random unlabelled paragraph
A table with columns for name, age and year level
A single unlabelled number
An unrelated image
12. Why is defining design criteria alongside a user story useful?
Criteria are irrelevant to user stories
Criteria help clarify exactly what the solution needs to achieve for that user need
User stories replace the need for any criteria
Combining them serves no purpose
13. A well-written user story should be:
Vague and unclear
Clear about who the user is and what they need
Written only for developers
Impossible to understand
14. Structured data with consistent attributes (like every "student" object having a name and year level) makes it easier to:
Confuse a database
Search, filter and query the data reliably
Randomise information
Delete all data
15. A development team skips decomposing a complex app idea and tries to build the whole thing at once. What risk does this create?
No risk, this is the most efficient approach
The complexity may become unmanageable, making it harder to design, build and test effectively
Complex problems are always easier without decomposition
Decomposition never has any benefit
16. Why might writing user stories before starting to build a solution help avoid wasted effort?
User stories have no real purpose
Clarifying real user needs first helps ensure development effort is focused on what is actually needed
Building without a plan is always faster and better
User stories only matter after development begins
17. A database stores student information as one unstructured block of text per student rather than as structured fields. What problem might this create?
No problem at all
Searching, sorting or analysing specific pieces of information becomes much harder
Unstructured text is always easier to search
Structured fields provide no benefit over plain text
18. Why might two different user stories about the same feature (from different types of users) lead to different design priorities?
Different users can have different needs and goals, requiring the design to address multiple perspectives
All users have identical needs always
User perspective has no bearing on design priorities
Only one user's story should ever be considered
19. When modelling a real-world entity as structured data, why is choosing the right attributes (fields) important?
Attributes have no effect on how useful the data is
Well-chosen attributes ensure the data actually captures the information needed for its purpose
Any random attributes work equally well
Structured data does not need meaningful attributes
20. Understanding decomposition, user stories and structured data mainly helps students:
Build solutions without any planning or structure
Break down problems clearly and organise information effectively when designing digital solutions
Ignore user needs entirely
Avoid structuring any data
Answer key (parent copy)
1. Breaking a complex problem into smaller parts
2. A user's perspective
3. "As a [user], I want [goal], so that [reason]"
4. Information organised into a consistent, predictable format
5. Name, year level and attendance
6. Manage and solve it more effectively
7. Structured data
8. Who needs it, what they need, and why
9. Smaller, well-defined parts are often more manageable to design and solve individually
10. Organise and process information about them consistently
11. A table with columns for name, age and year level
12. Criteria help clarify exactly what the solution needs to achieve for that user need
13. Clear about who the user is and what they need
14. Search, filter and query the data reliably
15. The complexity may become unmanageable, making it harder to design, build and test effectively
16. Clarifying real user needs first helps ensure development effort is focused on what is actually needed
17. Searching, sorting or analysing specific pieces of information becomes much harder
18. Different users can have different needs and goals, requiring the design to address multiple perspectives
19. Well-chosen attributes ensure the data actually captures the information needed for its purpose
20. Break down problems clearly and organise information effectively when designing digital solutions