Other Informatics (ITI) goodies
Informatics Software Tools
Unit 3 Software tools that students are required to both study and use in unit 3*
Software tools that students are required to use, but not study in unit 3*
Unit 4 Software tool that students are required to both study and use in unit 4*
Software tool that students are required to use, but not study, in unit 4*
* The ‘Advice for Teachers’ slideshow explains that: ” Software that is to be “STUDIED AND USED” have explicit reference made to the relevant software functions in the key knowledge and hence the skills in using this software are assessable Software that is said to be just ‘USED’ needs to be used by students, but is not part of the key knowledge and their skills in using the software are not assessed. What is assessed is the knowledge or skills that are demonstrated through the use of the software.
Design a solution, develop it using a relational database management system, and diagrammatically represent how users interact with an online solution when supplying data for a transaction.
|ITI U3O1 KK01 – techniques used by organisations to acquire data through their interactive online solutions and reasons for their choice
Websites and data This KK repeats KK 14 below – reasons why organisations acquire data using online facilities, including
ITI U3O1 KK02 – techniques for efficient and effective data collection
Data entry controls for GUI interfaces
|ITI U3O1 KK03 – characteristics of data types
Data-Types (link fixed 7 Sep 2016)
Data types are not defined for Informatics. The glossary says
“Data types are the particular forms that an item of data can take including numeric, character and Boolean, and are characterised by the kind of operations that can be performed on it. Depending on the software being used, these fundamental types can be divided into more specific types, for example integer and floating point are numeric types. More sophisticated types can be derived from them, for example a string of characters or a date type and their names may vary, such as text data type versus string data type. “
Common data types include:
Data types determine the storage requirements and properties of fields. For example, defining field DOB as type ‘Date’ not only lets the RDBMS allocate the exact amount of storage space, but it also notifies the database that it can perform date calculations with that field. If the DOB field had been defined as text, an operation like
ITI U3O1 KK04 – physical and software security controls used by organisations to protect their data
The main controls:
Physical – locked doors/windows, swipe card door keys, biometric readers (e.g. fingerprint scanner), UPS to protect servers from power outages, air conditioning for servers.
Software – usernames/passwords, encryption, two-factor authentication, firewalls, malware scanners, SMART (detects hard disk abnormalities), backup software.
Data Security (link updated 11 Sep 2016)
ITI U3O1 KK05 – purposes and structure of an RDBMS, including comparison with flat file databases
A flat file database only contains one table, and there are no relationships.
A spreadsheet is a flat file database.
ITI U3O1 KK06 – naming conventions to support efficient use and maintenance of an RDBMS
The study design does not name specific conventions, but here are the accepted norms:
Please remember that if you are asked to describe a filenaming convention, don’t just name it. Explain how it works, and – if relevant – why it is useful.
Field and table names (and forms, reports, queries etc) should be
Preferably do not use spaces or underscores in names – use dashes or CamelCase instead. Underscores are bad because they become invisible when the name is used in a hyperlink.
Most databases and programming languages forbid spaces in names since spaces indicate the end of the name.
|ITI U3O1 KK07 – a methodology for creating an RDBMS structure:
Database Referential Integrity (not assessable, but useful to know)
ITI U3O1 KK08 – design tools for describing data types and the value of entity relationship (ER) diagrams for representing the structure of an RDBMS
Entity Relationships Diagram (ERD) (updated version for 2016 with Chen, Crow’s feet and cardinality)
At long last, VCAA published its ERD exam conventions. In short, they accept both Chen (the style used in the last study design) and the ‘crowsfeet’ style. Any of the 3 styles may appear in the exam. You should also use one of these styles in your outcomes.
Note how cardinality (e.g. 1:many) is now officially included
Chen showing only top-level elements
ITI U3O1 KK09 – design principles that influence the functionality and appearance of solutions
The study design says in the glossary that:
Design principles are accepted characteristics that contribute to the functionality and appearance of solutions.
Design principles related to functionality are
Design principles related to appearance are
Design principles (new for 2016)
|ITI U3O1 KK 10 – design tools for representing solutions
Data Dictionary design tool
ITI U3O1 KK 11 – functions and techniques within an RDBMS to efficiently and effectively validate and manipulate data
Data validation checks the reasonableness of input data.
Validation can be both manual (e.g. proofreading) and electronic (e.g. running a spellchecker).
Mandated types of validation are not listed for Informatics, but they are for SD and you should know them:
Validation cannot and does not check the accuracy of the data. If a person said on a form that they were 18 whereas they were in fact 19, no database could discover the fault. The database could, however, detect that no age was provided or that the answer was “eighteen”.
Testing checks the accuracy of outputs and solution behaviours.
|ITI U3O1 KK 12 – functions and techniques to retrieve required information through
ITI U3O1 KK 13 – methods and techniques for testing that solutions perform as intended
Testing includes issues like:
|ITI U3O1 KK 14 – reasons why organisations acquire data using online facilities, including
|ITI U3O1 KK 15 – reasons why users supply data for online transactions, including
Websites and data (again)
|ITI U3O1 KK 16 – techniques used by organisations to protect the rights of individuals and organisations who supply data, including
Websites and data (again)
Security protocols include:
Stating policies regarding privacy, shipping and returns – so consumers are well – informed about their rights and responsibilities before committing to a transaction. Also consider:
|ITI U3O1 KK 17 – user flow diagrams [UFD] that depict different ways in which users interact with online solutions.
User Flow Diagrams (UFD) – new for 2016
The study design glossary defines a UFD as…
User flow diagrams are diagrammatic representations of the path a user travels through when using an online interactive solution to complete a task or transaction, such as making a reservation or purchasing a product. It is a diagram showing a user’s journey to complete a task. User flow diagrams incorporate user interfaces and show the multiple entry points to interactive online solutions, for example, paid advertisements, social media and search engines may direct a user to a location in the solution other than the home page.
The Advice for Teachers says…
The VCAA will not be mandating a specific style of user flow diagrams; however, it is important that the diagrammatic representations show a user’s interaction with an online solution when conducting a transaction, as well as the user interface for the page that initiates the transaction.
The Advice points to UFD examples at…
Here’s an example of one that I prepared earlier. The different shapes indicate different operations in a traditional flowchart, but since no rules apply you can use whatever format you choose in your UFD, as long as its meaning is clear and consistent.
This one appeared in the 2016 VCAA Informatics Exam…
Remember – U3O2 is part 1 of the SAT. It finishes in U4O1.
Use a range of appropriate techniques and processes to acquire, prepare, manipulate and interpret complex data to confirm or refute a hypothesis, and formulate a project plan to manage progress.
|Details about the Informatics SAT|
|ITI U3O2 KK01 – primary and secondary data sources (digital and non digital) and methods of data acquisition, including
Primary data is collected by the researcher for a specific purpose. It is not collected or processed by others. Primary data – if gathered properly – will be reliable and relevant to the research question. Its collection will, however, be expensive, slow, and of limited quantity. It will require considerable time, labour and skill for its processing. The main sources of primary data for the SAT would be questionnaires, surveys, and interviews. Another common method is direct observation of people acting naturally in their environment.
Secondary data has been collected by other people or organisations. It will also probably have been processed (validated, sorted, categorised, encoded, summarised) . Secondary data often includes opinions, conclusions or interpretations of the meaning of the data by other people. Secondary data may or may not be reliable or relevant. At worst, it may have been selectively chosen or misleadingly processed to support a particular point of view. Collecting secondary data is cheap, quick and easy. It is also available in huge quantities, and is the only way to collect data from the past. Secondary data is most commonly from: the internet; encyclopaedias; newspapers, magazines, TV, radio shows; reference books.
Note: sometimes the distinction between primary and secondary data is blurry. For example, a newspaper’s editorial on politics is clearly secondary data, but when it is used as data for an original purpose (e.g. tracking changes in newspaper’s political attitudes over time) it could qualify as being primary data.
‘Querying of resources’ refers to the practice of extracting data from datasets. Many government bodies (e.g. CSIRO, data.gov.au, Victorian government data) provide public data that can be searched by citizens. Also many organisations with large data repositories (e.g. Facebook, the weather bureau) provide an API (Application Programming Interface) that provides a gateway to let other people’s software extract data from the repository. In that way, for example, anyone can write an app that extracts today’s weather forecast from the met bureau’s dataset, or login to an online service using their Facebook credentials.
Queries take two major forms: Query by Example (QBE) and Structured Query Language (SQL). You do not need to know those terms, but they are handy to know.
QBE is the style used by GUI databases (Access, Filemaker etc) and spreadsheets where there are columns for each field, and rows that you can use to specify actions for those fields – for example selection and sorting criteria, and whether the field should be shown. Fill in the desired data into the appropriate fields and rows, and the software will – in the background – create a query that finds and manipulates the data required. For example:
SQL – Structured Query Language is a text-based instruction to a database that specifies all of the same requirements for data selection and manipulation that QBE generates. Often, the pretty QBE front-end simply generates SQL that the database then carries out. It’s a powerful scripting language for database queries. SQL looks like this (thanks to http://www.w3schools.com):
SELECT * FROM Customers WHERE Country='Germany' AND (City='Berlin' OR City='München');
Keep in mind that data extracted from an online query must still be considered secondary data. Even if it looks like raw data, it will probably have already been processed in some way, such as being summarised, sorted, categorised, and presented as percentages.
Repeat after me: Just because you found data online and extracted some with a query does not mean you created the data. It is not primary data. It is secondary.
Still to come – but the info above should keep you going for a while.
|ITI U3O2 KK02 – suitability of quantitative and qualitative data for manipulation including
Quantitative data is objective, measurable, and based on facts, e.g. temperature readings, numbers of daily visitors to a website.
Qualitative data is subjective, based on opinion, e.g. how people feel in a hot building, whether visitors enjoy visiting a website.)
Qualitative data is often collected at the start of research to discover what aspects of the topic are important and relevant. Once these are known, more detailed and specific quantitative data can be collected in much larger quantities.
For example, a company might want to know why sales of its boots are falling. Instead of guessing the reasons and creating a questionnaire or survey asking about those reasons, they interview people about their boots, or observe people trying their boots on in stores. They believed sales were falling because of cost, but discovered that people were actually put off by the comfort and dated style. The company could then use this qualitative data to formulate questions for a survey to fine-tune their understanding: specifically, what was uncomfortable? what styling seem dated? Without the preliminary interviews, the survey would have been asking about irrelevant factors and collecting irrelevant data.
Warning – to achieve full marks for U3O2 you must use both qualitative and quantitative data. This is buried in middle of the assessment rubric for criterion 2 and it not stated again in the highest performance descriptor.
Still to come
ITI U3O2 KK03 – data types and data structures relevant to selected software tools
Data types – just as boxes come in a variety of sizes, materials and shapes to suit their intended purposes (compare a shoe box with a matchbox, for example) so data structures come in a variety of types to store that type of data with minimum waste (of RAM or disk space), and processing effort.
Common data types supported by most relational databases are:
Different databases offer specialist, non-universal data types:
When creating a database, considerable thought needs to be put into choosing appropriate data types for the data to be stored. Selecting an inappropriate data type (e.g. storing dates of birth as text) will result in slow and difficult processing later. Lack of foresight can lead to a database ‘breaking’ later in its life, such as by choosing ‘byte’ as the data type for TotalMemberCount. Since a ‘byte’ field can only store values between 0 and 255, it will work fine until the member count reaches 256 – and then the database will freak out.
The Informatics study design is rather vague about which data types are examinable. The only help is the glossary that says:
Data types are the particular forms that an item of data can take including numeric, character and Boolean, and are characterised by the kind of operations that can be performed on it.
If I were you, I’d clean my room every day to keep mum happy, and learn the data types in bold above.
As for data structures… we can only guess what VCAA means because no data structure is named in the KK.
I would only be able to suggest the obvious:
But don’t worry. the exam can’t question you on things like double-ended priority queues or self-balancing trees because they not listed in the KK. The exam could only ask you to select a data structure and maybe explain what it is, in which case you’d want to choose one of the RDBMS or spreadsheet structures listed above.
|ITI U3O2 KK04 – one of the following methods for referencing primary and secondary sources:
All four referencing methods do the same thing – they help an author acknowledge the intellectual property of other people used in the author’s work. They only differ in their style.
Harvard and APA both use a parenthetical [i.e. in round parentheses] ‘author, year’ style in the body text such as
“…one researcher (Smith, 1980) claimed that…” and there would be a corresponding entry in the reference list at the end of the document such as
Smith, AB. The Life Cycle of Frogs. Frog Life Monthly, vol.65, num.11, 1980.
Chicago and IEEE use numbers in the body text to indicate links to the reference list, for example:
Chicago – “…one researcher 32 claimed that…”
IEEE – “…one researcher  claimed that…”
For all four methods there would be a corresponding entry in the reference list at the end of the document.
For Chicago and IEEE it would look like this:
32. Smith, AB. (1980) The Life Cycle of Frogs. Frog Life Monthly, vol.65, num.11, 1980.
For Harvard and APA, it would look something like this:
Smith, AB. (1980) The Life Cycle of Frogs. Frog Life Monthly, vol.65, num.11, 1980.
The differences between Chicago and IEEE are trivial – one uses superscript for the numbers. The other uses square brackets. Similarly, the differences between Harvard and APA are trivial – one may have a comma between the author’s surname and the year of publication whereas the other does not.
It does not matter which of the four referencing styles you use, but you must use one of the named styles, and you must use it consistently and correctly.
Do not mix different methods together in a reference. Choose one method and get it right.
As I interpret the key knowledge, you only need to be able to write references in only one of the four named methods.
Learn ONE style fully and use it consistently.
Still to come – but the stuff above is pretty good in the meanwhile.
|ITI U3O2 KK05 – criteria to check the integrity of data including
Note – unlike the previous study designs, in this study design, “timeliness” now means both/either:
Authenticity relates to how genuine the data is. Has it actually come from the named source? Has it been forged? Has it been distorted, for example: dishonestly edited, photoshopped, taken out of context, changed in any way to deceive the audience ? A statement that ‘there are four cows in the top paddock’ came from a person who never actually counted the cows, and just made up a number to save herself some effort or to avoid being punished.
Relevance – does the data relate to the issue being investigated? Data may be irrelevant because it’s
Accuracy – data is an abstract representation of real-world realities. To say to people that “There are four cows in my top paddock” is a more convenient representation of a fact than physically taking all the people to your top paddock and looking at the cows. If there actually are four cows in the top paddock, then the data is accurate. If there were actually three or five cows, the data would be less accurate. If there were four hundred cows, the data would not be at all representative of the true bovine quantity, and the data would be called inaccurate.
Data might be or become inaccurate due to
And let’s not argue about the difference between accuracy and correctness of data.
Still to come
Unexaminable bonus : Evaluating secondary sources can be difficult. There are some logical fallacies you might want to be aware of that are often based on arguments or evidence that are irrelevant to the issue in question (e.g. a correlation may be irrelevant to causation).
ITI U3O2 KK06 – techniques for coding qualitative data to support manipulation
Qualitative data is often textual, not numeric. It often comes as comments, statements, opinions that may include important information in all sorts of ways. Different people use different words, phrases, vocabulary to say the same basic idea. Coding this information is needed to reduce an infinitely-variable input of text into values that can be averaged, totalled and understood.
For example, in an interview, you might ask, “Has your business been affected by the opening of local supermarkets?” You might receive the following answers:
How could you summarise these variable-length responses meaningfully? Human interpretation is required to boil down the essence of a response into a limited range of possible answers, such as :
This encoding (or coding, as the study design prefers) of the free-form textual answers allows you to process the data statistically.
The drawbacks of encoding are:
One way of judging free-form responses more consistently is by using a rubric (not examinable) . A rubric lists descriptions of inputs and assigns them numeric values. VCAA outcomes and exams are assessed like this to ensure that different markers know what responses deserve.
A rubric may look like this:
The encoding effort and the risk of error during interpretation of qualitative data is why researchers often prefer to collect quantitative data using questionnaires with fixed choices of answers that do not require human interpretation.
The value of interviews and free-form answers, however, is the richness and depth of the answers that may yield valuable information that the researchers may not have ever dreamed of including as options. This is why early research often uses limited in-depth qualitative data collection (e.g. interviews, observation) to better understand what questions to ask and what answers to allow during later larger-scale quantitative research (with questionnaires and surveys)
Still to come
|ITI U3O2 KK07 – key legal requirements for storage and communication of data and information, including
Privacy – Privacy Act
Intellectual Property – Copyright
Human Rights Requirements – Charter of Human Rights
Note – The Spam Act is not listed as an Informatics requirement (as it is for SD). Strange, but true.
The only legislation named for Informatics is in U4O2:
The legislation relevant to this U3O2 KK is categorised but not named.
|ITI U3O2 KK08 – features of a reasonable hypothesis including a specific statement identifying
Prediction – a forecast of how the dependent variable will be affected by changes to the independent variable, e.g. “The less sleep students get, the worse their test results will be.”
A hypothesis must be able to make testable predictions. If it can’t, it’s pure speculation, or faith.
A hypothesis that “After good people die, they go to heaven” can never be tested, so it fails as a reasonable hypothesis.
Another hypothesis like, “Our entire universe is just a single atom in a larger universe!” cannot yield any testable prediction, so it also fails as a reasonable hypothesis.
Vague hypotheses (the plural of ‘hypothesis’) are unreasonable, for example “Dogs are better pets than cats because they’re more loving” cannot be measured scientifically. “Better” is an undefined and vague term. How can “loving” be quantified in animals? Does a dog’s licking mean love, or he’s tasting you to see if you’re worth eating? Does a cat’s purring mean love, or self-centred satisfaction in finding a warm lap on which to sleep?
And does it mean that every breed of dog and every breed of cat follow this rule, on only some of them?
A reasonable hypothesis is very specific. It has this format:
Independent Variable (IV) causes Dependent Variable (DV) to increase/decrease because reason Z.
A reasonable hypothesis cannot just vaguely say that one variable “affects” another. It must specify the nature of the effect.
It must specify one independent variable and one dependent variable and make sure that any other uncontrolled variables are removed from consideration during investigation. For example, if you look at two English classes and find that the smaller class has better grades than the larger class. You hypothesise that smaller class size (IV) causes learning (DV) to increase because students get more attention from the teacher.
But are there factors unaccounted for in this hypothesis? Could it in fact be that the difference is caused by:
The hypothesis must be able to make testable predictions, for example “If the larger class were divided into two classes taught by the same teacher, each half would get better grades.”
Also, you must not introduce new variables during investigation. For example, after dividing the large class into two, you should not give one half a different teaching style. If there were any any changes observed, you could never tell whether they were due to the size reduction or the change in teaching style.
A good investigation relies on the fact that any observed changes between groups can only be attributed to the independent variable and to no other cause.
Tip: this is wise during any problem-solving mission, such as finding out why your computer has started running slowly. If you change five things and the computer runs well again, how can you tell which change fixed the problem? Try to always test only one variable at a time. Control the others.
Still to come
|ITI U3O2 KK09 – solution specifications: requirements, including
In other words:
data: what data will needed to support the hypothesis (e.g. the numbers of new supermarkets openings; numbers of closures of small businesses in the same period; opinions of the small business owners regarding the effect of the supermarket opening on the closure of their businesses.
The distinction between constraints and scope in the key knowledge is a bit unclear. VCAA has not offered any example to clarify the difference.
In IT, constraints are usually factors that limit the free design of solutions, such as a limit on the total cost or time for development, the need for the solution to work on certain hardware, the requirement that the solution be very secure, or easy to use by complete idiots. Your research may be constrained by the public availability of relevant data, the number of reliable primary sources, the software you have available for data collection or processing.
Scope defines what is included in the research and what is not. For your SAT the scope may define how far will your hypothesis extends. For example, the hypothesis that supermarkets kill small local shops will only relate to
Still to come
ITI U3O2 KK 10 – project management concepts and processes, including the concepts of
Milestones are major points of progress in a project, for example the end of the design stage.
A dependency means that a following dependent task cannot be begun until a previous task has been completed.
The processes of
You must use a Gantt chart for the SAT.
ITI U3O2 KK 11 – file naming conventions to support efficient use of software tools
Good file naming means that names should :
Organisations and teams typically create file naming policies that all workers need to follow when sharing documents.
More details are in the slideshow…
|ITI U3O2 KK 12 – software functions to organise, manipulate and store data
This is so vague it could take a dozen books to cover completely. It refers to the commands that can be given in different software applications to handle data.
A typical exam question would be something like:
Jill has this pile of data. She wants a list of her shop’s top ten best-selling products (in order of popularity). Describe a strategy she could use to do this.
Organise: define and create fields, records and tables in a database. Create worksheets, columns, rows, lookup tables in a spreadsheet. Sort data. Put data into categories, tables. Convert data to a single unit (e.g. grams/kilograms all converted to grams, minutes:seconds all converted to seconds).
Manipulate: formulae in spreadsheets or database queries to work out basic arithmetic, totals, averages, maxima or minima, ranges, correlations, standard deviations. Produce charts. Pivot tables in spreadsheets.
Store: as files (e.g. Access/Filemaker/Excel proprietary formats, universal CSV, XML, RTF formats). Store as records in files or MySQL databases.
Note: The exam cannot force you to answer questions relating to spreadsheets (even if 99% of you guys used them for U3O2). The exam is entitled to ask specific questions about RDBMS.
Still to come
ITI U3O2 KK 13 – techniques for identifying patterns and relationships between data
This could be a biggie. We are talking about data trends and connections between different data sets. This implies concepts like:
A technique for identifying any trend within or between datasets is to reduce the huge bulk of raw data to a form where the trends are more easily visible. This is accomplished by summarising the data using statistics (e.g. averages and totals) and and visualising the data using data visualisation techniques (e.g. graphs and infographics).
My advice – read the textbook. I really don’t have the time to repeat all of that priceless wisdom here. But here’s a summary.
Averages (not examinable)
a summary of a larger set of data, showing its typical value. The problem is that one can summarise data in different ways and get very different answers.
For example, six men are asked how many lemurs they have. The answers are: 7, 9, 11, 6, 13, 6, 6, 3, 11.
Which of these different answers best summarises the numbers of lemurs owned by men?
For example, six people in a country are asked how much they earn each week . The answers are 30,30,60,170,1949.
If you were the country’s president trying to prove the well-being of your citizens, which average would you choose?
Moral – statistics can lie when you want them to. The use of statistics is just as important as the data the statistics are using.
Standard Deviation (not examinable)
Because the mean can be deceptive when the range of the data varies greatly, it is handy to know how much variance is in the dataset. The standard deviation (easily calculated by a spreadsheet, for example) is low when the data are consistently around the same value and the mean accurately describes their average value. A high standard deviation shows that the data vales vary greatly and have no consistent value. A high standard deviation indicates that the mean of the data is unreliable as a summary of the data. A high standard deviation is like a red light flashing the warning, “DON’T TRUST THIS MEAN!”
Using the sample data above, the lemur standard deviation was 3.01, indicating the data were pretty close to each other and the mean would be quite reliable as a summary of their values.
The income standard deviation was 752.3, warning that the mean would be wildly unreliable as a summary of the data.
Still to come. The stuff above should be enough for now.
|ITI U3O2 KK 14 – roles, functions and characteristics of digital system components used to
Hardware for input, storage, communication, and output (new for 2016)
|ITI U3O2 KK 15 – physical and software security controls suitable for protecting stored and communicated data.
Remember to cover all four parts of the KK. Physical/software and stored/communicated.
Remember – this is part 2 of the SAT that began in U3O2.
Design, develop and evaluate a multimodal online solution that confirms or refutes a hypothesis, and assess the effectiveness of the project plan in managing progress.
ITI U4O1 KK01 – characteristics of information for educating world – wide audiences, including
“In 2013, the Sex Discrimination Act 1984 was amended to introduce new protections from discrimination on the grounds of sexual orientation, gender identity and intersex status in many areas of public life.”
Sex – “refers to the chromosomal, gonadal and anatomical characteristics associated with biological sex. “(i.e. the hardware one is born with).
Gender – “is part of a person’s personal and social identity. It refers to the way a person feels, presents and is recognised within the community. A person’s gender may be reflected in outward social markers, including their name, outward appearance, mannerisms and dress.”
Intersex – “refers to people who are born with genetic, hormonal or physical sex characteristics that are not typically ‘male’ or ‘female’. Intersex people have a diversity of bodies and gender identities, and may identify as male or female or neither.”
The moral of this key knowledge is – don’t assume. Don’t exclude people because of their sex or gender. Be fair.
A culture is a defining characteristic of a group of people based on their shared beliefs, history, attitudes, religious or political beliefs, preferences, habits, loves and hates, priorities, goals, etc.
An individual may belong to many cultures. When writing for a global audience, try to be consciously aware that many or most readers will belong to cultures that may be slightly or completely differently to yours.
Don’t refer to politics, sex, religion – or football.
Commonality of language
We often use expressions and vocabulary that are bound to our cultures, but these may not be understood by some, many or all other people. Try to use generic, standard, simple English using a smaller vocabulary.
Obviously children have different needs when it comes to information. Their vocabularies may not be as well developed. They might not understand certain concepts, such as death or menopause. Some topics may scare them, like traumatic accidents or domestic violence. They might prefer text to be illustrated. Text may need to be larger to suit their younger eyes. Swearing is not appropriate.
Then again, older people have different needs. They might not be as technologically up-to-date so new terms may need to be defined or explained. They may know a lot more about things than you and be impatient with your vain self-importance and foolish time-wasting.
For more details, get the slideshow…
|ITI U4O1 KK02 – techniques for generating design ideas
No one technique is directly examinable, but typical and common techniques include:
|ITI U4O1 KK03 – criteria for evaluating alternative design ideas and the effectiveness of solutions
You need to develop two or three design ideas for your MMOS before choosing one which will be designed in detail and then developed.
What factors will you use to choose the winning idea?
Typical criteria for evaluating design ideas may be:
Typical criteria for evaluating the effectiveness of solutions may be (Note the word “effectiveness”. It does not include efficiency!):
The ‘effectiveness’ criteria list can go on endlessly.
|ITI U4O1 KK04 – characteristics of effective multimodal online solutions
Again, the key word in the KK is effective, meaning the quality of the MMOS or how well it does its job.
According to the specific criteria in the study design, the MMOS must
The other main effectiveness criteria for a MMOS would have to be (in no particular order – as an exercise you might want to sort these in order of importance):
Still to come
|ITI U4O1 KK05 – formats and conventions appropriate to multimodal online solutions
Format: the manner in which information is presented, e.g. the same statistical data could be presented in the format of a table, a chart, or descriptive text.
Convention: the standard, accepted styles associated with a format. For example, if you choose the format of a table, you are subject to following the conventions of a table, such as gridlines, bold headings, left-justified text, numbers being right-justified or centred on the decimal place.) Webpages conventions include the underlining of links, and the use of thumbnailed images linked to big pictures. Conventions give users comfort and security by presenting information and controls in a traditional, predictable and comfortable manner.
ITI U4O1 KK06 – design principles that influence the functionality and appearance of multimodal online solutions
The study design says in the glossary that: “Design principles are accepted characteristics that contribute to the functionality and appearance of solutions. In this study the principles related to functionality are useability, including robustness, flexibility and ease of use, and accessibility, including navigation and error tolerance. Design principles related to appearance are alignment, repetition, contrast, space and balance. “
|ITI U4O1 KK07 – design tools for representing a solution’s appearance and functionality, including relationships, where appropriate
This could take weeks. We’re talking about two major components: design of (1) appearance and (2) functionality.
Designing the appearance of a solution
Designing the functionality of a solution
Be sure to know the ones that are in bold.
This is a mandated type of functionality design. You need to know entity relationship diagrams (ERDs). VCAA’s interpretation of ERDs has changed since the previous study design. They now accept Chen style (the one with diamonds) that also has cardinality (1:many markers). They also accept Crow’s Feet Notation – the style with boxes (tables) containing fields with lines between the tables indicating relationships and cardinality. Both styles are acceptable, so you may be examined on either style: learn them both.
Entity Relationships Diagram (ERD) (updated version for 2016 with Chen, Crow’s feet and cardinality)
|ITI U4O1 KK08 – functions, techniques and procedures for efficiently and effectively manipulating data using software tools
This is another one of those KK that could have hundreds of textbooks written about it.
I guess some relevant topics might be:
*Remember, a procedure is a series of steps that accomplish a goal, e.g. backing up data. It is not a single action.
Still to come
|ITI U4O1 KK09 – manual and electronic validation techniques
Remember – validation checks the reasonableness of inputs (in terms of existence, data type and range).
It does NOT verify the accuracy of the input data.
|ITI U4O1 KK 10 – functions, techniques and procedures for managing files
This could include topics such as:
See the slideshows for details…
ITI U4O1 KK 11 – techniques for testing that solutions do what is intended
|ITI U4O1 KK 12 – techniques for documenting the progress of projects, including
Gantt charts are often complete and accurate when they are first created, but the real-world rarely lets a project stay on track for long. There are delays, problems, supply issues, breakdowns, illnesses and bad weather that can quickly render even the best Gantt chart a vain dream.
Project plans need to change, develop and reflect the true current state of the project. There is no shame in updating a Gantt chart, but there is foolishness to pretending that everything is on track when it is certainly not.
Gantt charts can be annotated to explain changes and forecasts to other team members, since a chart is rarely used by only one person in a project. Consider it more like a a staff bulletin informing departments and stakeholders what is happening. For example, a note might be added to explain why the last task ran overtime, and the effects it might have. Another note might warn people that if the weather on Tuesday is bad, task X will have to be postponed until Wednesday, so you should advice your staff to be prepared to start task Y instead.
Logs are a history of past events. In a project, they may be useful to explain to management why project plans were changed.
Adjustments to tasks and timeframes may include reducing the scope of a task to allow it to be completed enough to allow a predecessor to begin on time instead of being delayed. For example, a corridor may be painted but its wall hangings may not have been hung. The decorations could be made to wait if they were going to delay a more important task that was theoretically dependent on the entire corridor being finished (e.g. laying the carpet).
Timeframes can be also be modified by rearranging resources, such as moving people from one task to another to get a late-running task finished on time. New or reallocated resources may also accelerate a delayed task, such as using more automation (hiring a cement mixer rather than using shovels, as planned) or taking needed equipment planned for a later task (e.g. paint) and using it to finish a current task.
Project managers need to be able to adapt to changed circumstances and make the most of what is available, and use their project plan to coordinate many current and upcoming tasks.
Still to come
ITI U4O1 KK 13 – strategies for
Evaluation is not the same as testing. Testing proves that a solution works properly: it generates accurate information; if you click a link, the right destination appears.
Evaluation techniques also differ from those used during testing. Evaluation does not aim to repeat testing (e.g. take out a stopwatch and time how long it takes to produce 10,000 invoices, pulling out a power lead and seeing if the system can recover from an unexpected shutdown). Evaluation often relies on inspecting performance over time, such: as the total amount of output produced since the system was installed 3 months ago; the number of customer complains recorded about inaccurate billing. Evaluation often relies on studying performance over time – it does not record the system’s reaction to immediate events – that was the job of testing.
Note – if a system is producing inaccurate output or is failing to work properly, that is a sign that testing failed, and that the system should never have been implemented and put into daily service.
Evaluation (PSM stage 4)
Evaluation criteria (tangentially-related to this KK)
Compare and contrast the effectiveness of information management strategies used by two organisations to manage the storage and disposal of data and information, and recommend improvements to their current practices.
ITI U402 KK01 – reasons why data and information are important to organisations, including meeting the goals and objectives of both organisations and information systems
Systems – a combination of hardware, software, procedures, people and data that carry out a specific task within a department in an organisation, for example communications, decision making, financial management, customer service.
Organisations – the complete collection of departments and people within a commercial or not-for-profit entity.
Goals – long-term, fuzzily-defined things to be achieved by an entire organisation or system, e.g. “good customer service” for an org, “accuracy” for a payroll system. An organisation’s goals (e.g. in its mission statement) often defines the nature of the organisation and what it strives to achieve over time in everything it does in all its departments.
Objectives – specific, measurable, achievable targets that aim to achieve a larger goal, e.g. “respond to all customer enquiries within 24 hours” is an objective that is step towards achieving the goal of achieving good customer service.
Tip: objectives usually have numerical targets or limits in them, such as percentages, dollar values, time limits.
|ITI U402 KK02 – reasons why information management strategies are important to organisations, including
This is another of the vague and vanilla-flavoured KK that make everyone suddenly very sleepy.
Remember in Informatics “legal requirements” does not include the Spam Act.
Still to come
|ITI U402 KK03 – key legislation that affects how organisations control the storage and disposal of their data and information:
The Privacy and Data Protection Act 2014 controls how Victorian state government bodies (e.g. parliament, schools, police) and their agents (bodies hired to work with a government body) use citizens’ data. It does not apply to private companies or individuals.
The Health Records Act affects all Victorian organisations – private or government, large or small – that hold any health information on individuals. It basically repeats the requirements of the federal Privacy Act relating to health information.
This act affects doctors, nurses, psychiatrists, sports trainers, masseurs, hospitals, old people’s homes, psychiatrists, psychologists, health insurance companies, etc.
Data may include health histories, medications and prescriptions, diagnoses, mental health records, lab results, x-rays etc.
|ITI U402 KK04 – ethical dilemmas arising from information management practices
Also see the next KK.
Do you have other suggestions of ethical dilemmas in IT?
|ITI U402 KK05 – strategies for resolving legal and ethical tensions between stakeholders arising from information management practices
Just remember – if you are asked about an ethical dilemma, don’t try to convert it to a legal or rule-based issue to make life easier for yourself.
The whole point of ethical dilemmas is that they cannot be easily solved with a magic wand, such as a rule or law.
Ethical dilemmas are difficult because every possible reaction to them is bad for someone. They are lose/lose situations. You’re damned if you do, and damned if you don’t.
None of the following strategies is examinable!
|ITI U402 KK06 – reasons for preparing disaster recovery plans, and their scope, including
A DRP identifies steps to be performed in case:
…to name but a few contingencies.
What sorts of disaster might strike your valuable data?
According to a White Paper from IBM, the leading causes of data loss are:
And as time goes by, the dangers increase because:
So, just how disastrous can data loss be?
IBM reported that, “Fifty percent of companies that lose critical business systems for 10 or more days never recover.”
For most companies today, data is their business. If that data is lost or corrupted, or merely interrupted for a long enough period, the blow to the company can be fatal. Studies show truly disastrous results for businesses that lose access to data.
When businesses in the following fields lost access to their data for the given time periods, 25% suffered immediate bankruptcy; 40% went bankrupt within two years; and almost all were bankrupt after five years.
And how much does it cost to recover data? MASSIVE AMOUNTS – at least $1000 per megabyte. Data must be manually found or re-created, re-entered, validated, tested, updated. And remember that there are not many paper records nowadays – most data may never be recoverable. It takes a lot of labour and time – and normal profitable business is probably impossible to conduct until the data is restored.
Building a DRP
Let’s imagine there is a fire at your office… You should ring the office manager, but you don’t have her home phone number. You need to ring the insurance company immediately to get the destroyed equipment replaced, but you can’t remember what company insures you or where the policy is (oh… dear. You remember: the policy was in the filing cabinet your burnt out office.) You need to rent emergency equipment to get back into business… but you can’t remember the phone number of that company either. You need to get your backup tapes to restore the file server’s data… oh no…the backup tapes were in the filing cabinet with the insurance policy. At least you can get a copy of your recovery plan and… oh dear. The only copy of the plan was stored on the file server. You really are up the proverbial brown creek…
What do you do to avoid this cruise down a smelly waterway?
You print out your draft disaster recovery plan and read it. You discover it’s out of date and does not cover many of the problems you faced in your nightmare. You get a team together from management, IT staff and office staff and update and complete the plan.
What should a good DRP achieve?
Testing the DRP
Unless you test your DRP, you will never sleep soundly. What if the plan fails when it’s most needed? Make sure it doesn’t. Test it.
Still to come – or not.
|ITI U402 KK07 – possible consequences for organisations that fail to follow or violate security measures
Still to come. Maybe. Probably not, though.
ITI U402 KK08 – criteria for evaluating the effectiveness of information management strategies
Note two key words – “evaluating” and “effectiveness”.
Evaluating is done after testing and development and implementation of a solution. The product has already been proved to be working properly during the testing phase. Evaluation is NOT A SECOND TESTING STAGE!
Evaluation determines if/how well a solution is achieving the goals for which it was originally created. It does NOT determine whether it produces correct output or behaves properly. A website created to increase profit may work 100% accurately but still not create any extra profit: its testing says it’s perfect (no errors!) but its evaluation shows it’s a failure (no profit!).
Evaluation occurs after a solution’s rollout to its users. It may begin some months after users start using it for real. The delay is to give users time to become familiar with the solution so they can judge it knowledgeably.
Evaluation criteria are the topics you use to judge a solution’s level of success or failure. Each solution will have different criteria, based on what it vital for it to achieve.
Effectiveness criteria – You only need to remember that EFFICIENCY criteria comprise measurable TIME (SPEED), COST, LABOUR (EFFORT). Every other criterion is effectiveness. (There. Easy! Aren’t you glad you came to vceit.com?) Effectiveness criteria are often opinion-based and endless, and relate to how well a solution does its job in terms of: accuracy, readability, ease of use, security, robustness, attractiveness, fun, portability, etc.
Evaluation criteria are determined during the design stage of the PSM. These are used during evaluation to determine if the project has been successful or not. Lessons may be learned to improve the next project.
Still to come
ITI U402 KK09 – role of people, processes and digital systems in the management of data and information
This KK dotpoint could launch a dozen textbooks.
Roles of people:
Try this site for a few more details about ICT jobs and roles.
Roles of processes – none of these is examinable, but I’m getting to sleepy to care.
Digital systems – people, hardware, software and networks, communication protocols, intranets, internet, mobile communications, VPN, wired/wireless, operating systems, users, data, architectures (thin/thick client).
Roles of hardware:
There. Wasn’t that quick and easy?
Network hardware (servers, NICs, modems, RAID, etc)
ITI U402 KK 10 – types and causes of accidental, deliberate and events-based threats to the integrity and security of data and information
ITI U402 KK 11 – physical and software security controls for preventing unauthorised access to data and information and for minimising the loss of data accessed by authorised and unauthorised users
We’ve done this earlier in U3O1 KK04 – physical and software security controls used by organisations to protect their data.
ITI U402 KK 12 – the advantages and disadvantages of using networks and cloud computing for storing and disposing of data and information.
Here are some suggestions. There are many other valid possibilities to consider.