Flashcards
Data, Information and Knowledge PPQ
Flashcards do conjunto (8)
- 11. Define the terms Data, Information and Knowledge. Use an appropriate example to demonstrate the relationship between each of the terms. [6]Data consists of raw facts and figures.
Information is data which has been processed by the computer (given context
or meaning).
Knowledge is derived from information by applying rules to it.
Any appropriate example, such as 9:00, 135/75, 10:00, 135/75, 11:00,
120/60.
To render the data as information, it first must be given a context, e.g.
medical – a patient’s blood pressure reading or to make it more
understandable it is put into a table.
Reading time Patient’s BP
9:00 135/75
10:00 125/70
11:00 80/60
The doctor can now apply her knowledge to this information. The patient’s
blood pressure is dropping (information) and their condition is deteriorating
(knowledge).
(Do not give mark if the figures are totally unrealistic) - 21. (a) Define the term knowledge. Using a suitable example, illustrate the relationship between data, information and knowledge. [4]Knowledge is derived from information by applying rules to it.
Data is 120/60, 135/65, 140/70
Information these are blood pressure readings
Knowledge patients conditions worsening
Or similar examples
Data is L1 56.4, L2 57.5, L3 54.5, L4 57.9, L5 64.4. L6 55.1
Information these times are from a swimming race
Knowledge is that the winner was in Lane 3 - 3Other than ‘it takes less time to type in’, give two reasons to explain why data is encoded before being entered into a computer system. [2]Any two from:
• fewer transcription errors / greater data consistency / easier to validate
• less hard disk space required / takes up less room on the hard drive / less memory needed
• Processing is faster (because less RAM required) / faster to search (pattern matching)
• CONDONE More data can appear on the screen
NOT faster to spot trends
Encoding is NOT encryption - 4By referring to an appropriate example, describe one problem associated with encoding of data. [2]Problem
Encoding can coarsen data
Limited choice leading to less accurate data
Limited choice leading to loss of precision
Example -
Not enough categories when representing eye colour, or age groups (when finding the mean
have to assume all at the midpoint for grouped data) BUT not just answers like bracketing
34 year olds with 26 year olds causes problems.
Value judgements fitting into a category and subjectivity / Value judgements can lead to
inconsistency e.g. hair colour, opinion on politicians, etc.
Examples of one mark answers
Coarsening data leading to loss of precision
Limited Choice leading to less accurate data
Value judgements can lead to inconsistency
Examples of two mark answer
Coarsening data can lead to loss of precision if dark brown, mousy brown, light brown are all
classed as brown.
Value judgements can lead to inconsistency for example if asked “Was the meal ‘excellent’,
very good’, ‘good’, or ‘poor’?” One person’s excellent meal is only good for another. - 52. Data is often encoded before it is entered into a computer. One of the advantages of encoding data is that it saves time typing in the data. (a) Give two other advantages of encoding data before entering it into the computer. [2]One mark per advantage from:
• Fewer transcription errors / greater data consistency / easier to validate
• Less hard disk space required / takes up less room on the hard drive / less memory needed
• Processing is faster (because less RAM required) / faster to search (pattern matching)
NOT More data can appear on the screen
NOT less space NOT storage space
NOT faster to spot trends
NOT easier/faster to enter data than to type the whole word.
NOT anything connected to security.
NOT less chance of getting rsi.
NOT easier to query a database
Encoding is not encryption. Do not accept ‘typing errors’. - 6(b) Encoding data can lead to inaccurate results. Identify two different ways this could happen and illustrate each with an appropriate example. [2×2]Problem
Encoding can coarsen data
Limited choice leading to less accurate data
Limited choice leading to loss of precision
Value judgement
Example
Not enough categories when representing eye colour, or age groups (when finding the mean have
to assume all at the midpoint for grouped data) BUT NOT just answers like bracketing 34 year
olds with 26 year olds causes problems.
Value judgements fitting into a category and subjectivity / Value judgements can lead to
inconsistency, e.g. hair colour, opinion on politicians, etc.
Examples of one mark answers
Coarsening data leading to loss of precision
Limited Choice leading to less accurate data
Value judgements can lead to inconsistency
Examples of two mark answer
Coarsening data can lead to loss of precision if dark brown, mousy brown, light brown are all
classed as brown.
Value judgements can lead to inconsistency for example if asked “Was the meal ‘excellent’, very
good’, ‘good’, or ‘poor’?” One person’s excellent meal is only good - 71. Data is often encoded in order to store it more efficiently in a database. One advantage of encoding data is that it takes up less memory on the hard disk. (a) Give two other advantages of encoding data before it is entered into a database. [2]One mark per advantage (Max 2) from:
• Fewer transcription/typing errors or greater data consistency or easier to
validate (what or a why).
• Faster to enter/type in.
• Processing is faster (because less RAM required) or faster to
search/query (pattern matching).
NOT More data can appear on the screen
NOT less space NOT storage space
NOT faster to spot trends
NOT anything connected to security.
NOT less chance of getting RSI.
NOT easier to query a database
Encoding is not encryption. Do not accept just ‘typing errors’. - 8(b) Give two problems associated with encoding data and for each give a different appropriate example. [2×2]Problem
Encoding can coarsen data
Limited choice leading to less accurate data
Limited choice leading to loss of precision
Value judgement
Example
Not enough categories when representing eye colour, or age groups (when
finding the mean have to assume all at the midpoint for grouped data) BUT
NOT just answers like bracketing 34 year olds with 26 year olds causes
problems.
Value judgements fitting into a category and subjectivity / Value judgements
can lead to inconsistency, e.g. hair colour, opinion on politicians, etc.
Examples of one mark answers
Coarsening data leading to loss of precision
Limited Choice leading to less accurate data
Value judgements can lead to inconsistency
Examples of two mark answer
Coarsening data can lead to loss of precision if dark brown, mousy brown,
light brown are all classed as brown.
Value judgements can lead to inconsistency for example if asked “Was the
meal ‘excellent’, very good’, ‘good’, or ‘poor’?” One person’s excellent meal
is only good for another.
NOT unable to understand the code or mixing up the code
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- AS level ICT FlashCards sobre Data, Information and Knowledge PPQ, criado por rlouise em 02-03-2015.
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- rlouise
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