QCE Digital Solutions Unit 2 — Data-Driven Problems and Solution Requirements Hangman

The 128 key terms

1data

Raw facts and values, like names or scores. When data is organised so it has meaning, it becomes information, like a table showing each class's average score.

2data-driven problem

A problem whose solution depends on collecting, storing, processing and presenting data, such as keeping records or reporting results. Problems needing no data are not data-driven.

3primary data vs secondary data

Primary data is data you collect yourself, like a survey. Secondary data was collected by someone else, like data from a website or database.

4Quantitative data

Numerical data that can be counted (discrete, like number of students) or measured (continuous, like temperature) and used in calculations, such as ages, scores and temperatures.

5Qualitative data

Descriptive data such as opinions, comments and experiences, often from open-ended responses or interviews. It is not measured as numbers, and is sorted into categories and descriptions.

6data requirements

A list of the data items, types, sources and amount needed to answer a research question, such as the variables, primary or secondary source, time period and sample size.

7file format

The way a dataset's data is structured and stored in a file, such as CSV, XLSX, JSON or XML. Choose the format that suits the software you will open it in.

8CSV

A text file with one record per line, fields split by a delimiter such as a comma, and an optional header line of field names. Text containing commas goes in quotes.

9TXT file

A plain text file holds unformatted characters, usually one line of text per line ended by a newline. It has no field headings or data types, so everything is read as a string.

10<student><name>Mia</name></student>

XML stores data in nested, named elements. Each element has an opening and closing tag, and child elements sit inside a root element. The tags describe the data.

11TXT, CSV, XML

Choose a data source by structure: TXT for simple lines, CSV for tables of records, XML for nested self-describing data shared between systems. All three store data between runs.

12inconsistent data format

Data from different sources written in different ways (dates, text vs numbers, delimiters). It must be converted to one consistent format before it can be inserted into a data store.

13one-dimensional array

A single row of items of the same data type, stored under one name. Each item is found by its index number, starting at 0. In Python, a list is used as an array.

14record

A structure that groups related fields of different data types to describe one thing, such as a student's name (text), age (integer) and mark (decimal).

15grid[row][col]

A 2D array stores data in rows and columns like a table. Use the row index first, then the column index, to reach one element, such as a seat in a seating plan.

16list, dict, tuple

Pick the structure by how you use the data: a list for an ordered collection, a dict for lookup by key, and a tuple for a fixed group of values that should not change.

17validation

An automatic check that data is reasonable and in the right form when entered. It blocks bad data early, but it cannot prove the data is true (a valid age of 15 could still be wrong).

18validation check

A test that an entry is sensible. Types include presence, data type, range, length, format and list. Each one tests a different property of the entry.

19validation vs verification

Validation checks entered data is reasonable and complete. Verification checks it matches the source data, for example by proofreading.

20reasonableness

Data is reasonable when its value is sensible and believable in its context, so impossible values or odd outliers (like an age of 250) are flagged by reasonableness checks.

21decomposition

Splitting a big problem into smaller sub-problems, such as input, process and output. Each part can be solved on its own, then the solutions are combined.

22sub-problem

A small part of a bigger problem. It is easier to understand and solve than the whole, so splitting a problem up makes it manageable and makes mistakes easier to find.

23pattern recognition

Spotting similarities or repeated features in sub-problems so that one solution can be reused for similar tasks, such as repeated data entry steps or similar record structures.

24Input, Process, Output, Storage

Read the scenario and sort every part into input (data in), process (what is done), output (result shown) or storage (data kept). Then check nothing is missing or in the wrong group.

25Data collection method

A structured technique, such as an interview, survey, or observation, used to gather information about user needs before designing a solution

26interview

A conversation where you ask one user questions and listen to their answers to learn about their problem.

27good interview question

A question that is open, neutral and about the user's real task, so it draws out detail instead of a yes or no. Ask 'How do you...?' then follow up with 'Why?' Don't lead them to an answer.

28observing

Watching a user do a task to see where they get stuck, without helping or interrupting. You write down what you see instead of just asking them what they think.

29thematic analysis

Sorting similar interview and survey responses into themes, then counting how often each appears, so the most common problems and wishes become the user needs.

30The solution must

Starts a functional requirement: one thing a digital solution must do. It says what the solution does, not how, and covers one action. Example: The solution must show the score.

31Non-functional requirement

A quality describing how well a system performs, such as speed, security, or reliability, rather than what it does

32measurable non-functional requirement

A quality of a solution, such as speed, security or usability, written with a testable target, like a page loads in under 2 seconds, so you can check whether it is met.

33constraint

A limit on how a solution can be made, such as time, cost, skills, devices or software. A constraint is not a feature; it limits what you can build.

34scope

The boundary of what a solution will and will not include. Features inside the boundary are in scope. Features left out, or saved for later, are out of scope.

35requirements list

A list that sorts what a solution must do (requirements) from the limits it must work within (constraints), for one problem. Each item should be clear and testable.

36prioritising requirements

Ranking requirements as must-have, should-have or nice-to-have so the most important needs are built first, and trade-offs are made when time or budget is limited.

37input

Data that goes into a system from a user, a sensor or a file, such as a typed name, a temperature reading or a line read from a stored file.

38process

An action or calculation a solution carries out on its data, such as adding a total, comparing a value with a limit, sorting or searching, or making a decision.

39output

The result a solution gives out, such as a message on screen, a printout, a sound or a signal that switches a device on or off.

40storage

Data the solution must keep, in a variable, file or database, so it can be used again later. It is not the same as input or output.

41IPO chart

A three-column table listing a solution's inputs (data entered), the processes applied to that data, and the outputs it produces, such as a total or report.

42string

Text made of a sequence of characters (single letters, digits or symbols), written in quotes, like 'Sam' or '0412345678'. Phone numbers are strings.

43integer

A whole number with no decimal part, positive or negative, used for counts such as the number of students in a class (e.g. 25, 0 or -3).

44float

A floating point number is a number with a decimal part, such as 1.75 or 19.95. It is used for measurements like height and for money, unlike an integer, which has no decimals.

45Date/Time

A data type that stores a calendar date and/or time of day, such as 25/03/2025 or 14:30, so values can be sorted in order and used to calculate the duration between two dates.

46Boolean

A data type with only two possible values: true or false (yes or no). Used for yes/no fields, TRUE and FALSE in a spreadsheet, and the result of a comparison such as 5>3.

47data type

Pick the data type by what you will do with the value: calculate (number), sort by date (date/time), test true or false (Boolean). Postcodes are text, not numbers, as you never do maths on them.

49VARCHAR(30)

Field size sets the maximum length or storage a value may use. VARCHAR(30) allows up to 30 characters; longer values are rejected or cut. Smaller sizes save storage.

50CHECK (age BETWEEN 0 AND 120)

A constraint is a rule on a column. This CHECK rejects any record with an age outside 0 to 120. NOT NULL (must have a value) and UNIQUE (no repeats) are constraints too.

51entity

A type of thing a database stores data about, such as a student, book or order. Each entity becomes one table, and its name is a singular noun.

52attribute

An attribute is one property of an entity, like a student's name or year level. Each attribute becomes one field (column) in the entity's table, and each instance of the entity is one record.

53database

A database gives every record the same fields and data types and lets you query large amounts of data; a spreadsheet is a flexible grid.

54database table

A database table stores data in columns called fields and rows called records. A single-table database keeps all its data in this one table.

55data redundancy

The same details stored again and again in one big table. It wastes space and risks typos or missed updates, like a customer's address copied onto every order.

56table design

Planning a table by listing a clear field name and data type for each piece of data, with one fact per field, such as FirstName and LastName as two fields.

57table structure

The plan for one table: list each field the scenario needs, give it a data type and any validation, and choose a primary key so the table can hold every record.

58data dictionary

A table listing each data element with its name, data type, size or format, description and validation rule, so everyone uses the data the same way when coding and testing.

59studentAge, integer, 2 digits, age in years, 15, 11 to 19

One data dictionary row for a data item: name, data type, format or size, description, example value and validation rule. Written from a requirement or IPO chart.

60PRIMARY KEY

A field whose value is unique for every record in a table, such as a student ID number, so each record can be identified exactly. Names make poor keys because they can repeat.

62relational database

A database that stores data in separate tables of fields (columns) and records (rows), with tables linked by shared values such as keys.

63FOREIGN KEY

A field in one table that holds the primary key value of a record in another table, linking the two tables together so related data is stored once and not repeated.

64one-to-many relationship

One record in a primary table matches many records in a related table. The foreign key sits on the many side, e.g. one class has many students, so class details are not repeated.

65junction table

A table that splits a many-to-many relationship into two one-to-many links by holding two foreign keys, such as Enrolment with studentId and subjectId.

66referential integrity

Every foreign key value must match an existing primary key in the related table, so no record points to something missing. The database blocks changes that break this.

67ERD

An entity relationship diagram draws each entity as a labelled box and each relationship as a line joining two boxes. Boxes list attributes, with PK and FK marked.

68crow's foot

ERD notation where a single bar at a line's end means one and a three-pronged crow's foot means many. The crow's foot end is the foreign key side of the relationship.

69ERD from a scenario

Find the entities, list their attributes, pick primary keys, then work out each relationship and draw it, with the foreign key on the many side.

70input control

An on-screen element, such as a text box, drop-down list, option button or checkbox, that lets users enter data. Each suits a different kind of data.

71data presentation

Showing data to users as a table, list, form, report or chart, chosen to suit the data and the user's task, and showing only the data they need.

72feedback

A response from the interface that tells the user what happened, like a confirmation message, or an error message that says what went wrong and how to fix it.

73user flow

The path of screens a user follows to finish a task, like logging in or buying a ticket. Navigation controls such as back and home buttons help them move along it in as few steps as possible.

74output requirement

A statement of what information a solution must produce, in what format and medium, for which audience, and how often it is produced.

75user experience (UX)

How easy, efficient and pleasant a system feels to use from start to finish, not just how it looks. Good UX avoids frustration points, while UI is only the look.

76usability

How easily, quickly and accurately users can learn and use software to achieve their goals, including ease of learning, efficiency of use, and error prevention and recovery.

77affordance

When the look of a control signals how it can be used, such as a raised button that looks pressable or a slider handle that looks draggable. Clear visual cues make actions obvious.

78accessibility feature

A design choice that lets people with different abilities use a system, such as alt text for images, captions for video, keyboard-only controls or colour-blind safe colours.

79efficiency

How well a solution uses resources such as time, processing, memory, storage and user effort to do its job. An efficient solution is fast, light on memory and storage, and saves the user time.

80Effectiveness

How well a solution does what it was meant to do, giving correct, complete and usable results that meet the requirements and suit its purpose.

81error tolerance

An interface design quality where mistakes are prevented, reasonable input variations are accepted, and users can easily recover, such as with undo, confirmations and clear error messages.

82UX characteristics

Affordance, interoperability, security and usability. Designers review a solution against each one and balance trade-offs, such as stronger security making a system harder to use.

83analytical tools

Diagrams like context, data flow and use case diagrams that show how users, data and systems relate, so the existing system is understood before a solution is designed.

84context diagram

A Level 0 diagram that shows the whole system as one process in the centre, with external entities around it outside the system boundary.

85data flow

A labelled arrow on a context diagram showing data passing between an entity and the system. The arrowhead shows direction (input or output) and the label names the data.

87Level 1 DFD process

A numbered, named step (e.g. 1.0 Validate Order) in a Level 1 DFD. It uses a verb phrase and shows one main task from the context diagram's single system.

88Data store

On a Level 1 DFD, a data store is where data is held (e.g. a file or database). Entities sit outside the system and send or receive data. Both must match the context diagram.

90Level 1 DFD

Breaks a system into processes, data stores, entities and labelled flows found in a scenario, then checks the rules, e.g. every process has an input and an output and no flow is missing.

91Pseudocode

A plain-language, code-like way of planning a solution's logic without following the exact syntax of a real programming language

92end if

A closing keyword in VCAA pseudocode that ends an if block; every nested construct uses end followed by its matching keyword: end if, end while, end for

95print

A VCAA pseudocode keyword that outputs a value, variable or expression as a result. Example: print area displays the current value stored in the variable area

96MOD

The pseudocode operator that returns the remainder after integer division. Example: 10 MOD 3 gives 1 because 10 divided by 3 is 3 remainder 1

98AND

A VCAA pseudocode boolean operator that combines two conditions. The overall result is true only when BOTH conditions are true. Example: x > 0 AND x < 10

100IF-THEN-ELSE

A selection structure that runs one set of instructions if a condition is true, and a different set if it is false

101ELSE IF

A keyword that adds another condition to check if the previous IF or ELSE IF condition was false

102FOR i FROM 1 TO n

A loop that repeats its instructions a fixed number of times, counting the variable i from 1 up to n

103WHILE condition DO

A loop that repeats its instructions for as long as the given condition remains true, checked before each repetition

104REPEAT ... UNTIL condition

A loop that repeats its instructions and checks the condition after each repetition, so the body always runs at least once

105count <- count + 1

A counter. It adds 1 to a variable each time a loop repeats. Set it to 0 before the loop. An accumulator works the same way: total <- total + x adds a value each time.

106scores[1]

Accesses the array element at index 1, using square brackets to specify the position.

107student.name

Accesses the name field of the student record, using a dot to connect the record to its field.

108define

A keyword that starts a function definition, naming the function and listing the parameters it accepts

109return

A keyword that sends a value back from a function to where it was called, ending the function's execution at that point

110IPO to pseudocode

Turn each IPO input into an INPUT statement, each process into assignments or IF selection, and each output into a PRINT statement, in that order.

111personal data

Information that identifies you or can be linked to you, such as your name, birthday, address, photos or school. A favourite colour on its own is not personal data.

112sensitive information

A special kind of personal information, like health, ethnic origin, religion or biometric data, that the Privacy Act protects more strictly because misuse can cause serious harm.

114social impact of government databases

How large government databases affect people: they can improve access to services, but raise surveillance and trust concerns and risk excluding or profiling groups.

115data exposure risk

The more data a system keeps, the more harm if it is stolen or leaked. Extra stored data makes identity theft and misuse of that data more likely and more damaging.

116individual impact

The harm a cyber attack causes a person, such as stolen money, identity theft, a locked-out account, lost private photos or messages, reputation damage and emotional distress.

117organisational impact

The harm a cyber attack does to a business or group, such as downtime, recovery costs, lost customer trust, a damaged reputation and fines for breaking privacy laws.

118financial loss

Money lost when corrupted or incorrect data causes errors, rework, refunds and lost sales, such as wrong payments and the cost of fixing the data.

119Poor decision-making

Decisions made from inaccurate or untimely data are likely to be wrong, causing wrong forecasts, misleading analysis and false conclusions.

120Ownership and control of data

Who has the right to hold, change, share and delete data about a person or topic. An organisation may hold the data, but the person it describes should keep some control.

121Misuse of personal data

Using or sharing personal data in ways the person did not agree to or that harm them, such as selling it, identity theft, or using it for a different purpose.

122line

A mark that connects points. It guides the eye, divides space or shows structure, and can be straight, curved, thick or thin, as in borders and dividers.

123shape

A flat, enclosed area with a clear edge, like a button or panel, that groups or highlights content. Shapes can be geometric (circles, squares) or organic (free-form).

124colour palette

A small, consistent set of colours where each colour has one meaning. Use the same colour for the same category, and never rely on colour alone, so colour-blind viewers can still read it.

125texture

The look or feel of a surface, shown with patterns or fills, used to add depth or tell areas apart. It can be real (felt by touch) or simulated (only looks textured on screen).

126tone

Tone is how light or dark an area is. Designers use it to create contrast, depth and emphasis, for example light text on a dark background, or a greyscale shade to show importance.

127form

The appearance of a three-dimensional object, or the illusion of depth on a flat screen, created using shading and perspective.

128space

The empty area around and between interface elements, used to group related items, separate unrelated ones and keep a layout uncluttered and easy to read.

129proportion

The relationship in size between the parts of a design and the whole, such as how large one element is compared with the others around it.

130scale

The overall size of an element compared with a reference, such as other elements or the page. Enlarging or reducing it shows importance, so bigger elements draw the eye first.

131balance

The even spread of visual weight across a screen so no side feels too heavy. It can be symmetrical (mirrored) or asymmetrical (different elements that still feel equal in weight).

132contrast

The difference in colour, size or brightness that makes key elements and text stand out from their background so they are easy to read and notice.

133proximity

A design principle: place related items close together and unrelated items further apart, so users see the groups clearly. Eg group name fields, then leave space before the next group.

134harmony

Harmony is when the elements of a design, such as colours, shapes and style, work together so the visual feels unified and pleasing rather than clashing.

135alignment

A UI design principle that lines up elements along common edges or centres, such as a left edge or grid, so the layout looks tidy and is easy to scan.

136consistent design

Using the same colours, fonts, alignment and spacing on every part of a dashboard or poster series so it looks unified and is easy to read.

137visual hierarchy

Using size, colour, weight and position so the viewer's eye goes to the most important element first, then follows a clear reading order to the rest.