​Unit 3: Data analytics

Area of Study 2: Data analytics: analysis and design

Outcome 2

Propose a research question, formulate a project plan, collect and analyse data, generate alternative design ideas and represent the preferred design for creating infographics or dynamic data visualisations.

Examples of learning activities

• Revise knowledge of the role, function and characteristics of digital systems by creating a resource list of hardware, software and network requirements for each deliverable of the School-assessed Task.
• Document the physical and software security controls that are used to protect stored and communicated data at your school.
• Develop an understanding of the data collection methods for primary sources. The first method is to use a survey requiring the use of an online tool such as Google Forms or Survey Monkey. The second method involves writing questions for a face-to-face interview to be recorded on paper. The survey questions should take advantage of the strengths of each data collection method. Conduct each of the surveys and interviews with a small sample of students and compare the quality of the responses.
• Students are given a list of data requirements from the Australian Bureau of Statistics, such as census data showing the population changes in Victoria from 2000–2020 (predicted). They are encouraged to use these in the following activities:
  
  • search for data (through either the search tool, or finding the topic on the ABS home page) relating to the data requirements
  • browse through a number of tables to find data in the required structure and format
  • download into a file that can be imported into a database.
• Teacher demonstrates how quantitative data and qualitative data could be visualised. Discuss the difficulties in visualising qualitative data. An example would be asking two questions to the class – ‘What is your favourite day of the week?’ and ‘Why is this your favourite day of the week?’ The data for the first question, being a closed question, could be easily transformed into a data visualisation (bar/column/pie chart) with categories of Monday, Tuesday, Wednesday, etc. The data for the second question, being an open question, will most likely need to be grouped and summarised (using a process of coding of qualitative data) before a similar visualisation can be created.
• Students complete the following two activities to understand the progression from extracting data from a database into a spreadsheet form and then creating information.
  
  • Compare the structure of a database (fields, records, highly structured) to a spreadsheet (rows, columns, loose structure) and list advantages and disadvantages for each structure type.
  • Use a spreadsheet and create a unique tab which shows the format in which data needs to be structured in order to create different types of data visualisations (e.g. column/bar chart, line chart, radar chart, world map, heat map).
  By the end of this activity, students should have confidence to be able to process their own data from a database to a data visualisation using the flexibility of a spreadsheet to manipulate the data structure.
• Students complete the tutorial exercises to build knowledge of referencing requirements, including in-line citations, bibliographies and plagiarism avoidance. They use samples of authentic sources of information from a variety of types of data (newspaper article, book, online article, dataset, journal article, video file) to create a reference list using the APA referencing method.
• Discuss samples of data that include high integrity and low integrity and a rubric based on data integrity criteria. Students use this rubric to assess the integrity of the data and to suggest methods that can be used to improve the data.
• Using sample statements for an IT helpdesk receiving feedback for support calls that have been completed, students read each statement and allocate a numerical value to each one, from one (highly negative) to five (highly positive). This process is an example of coding qualitative data to support manipulation, called sentiment analysis. Students who wish to complete an extension exercise can explore using automated techniques to generate sentiment analysis results, such as the sentiment package in the R programming language, and compare this to manual forms of sentiment analysis judging the tone (positive or negative) of a response. The following resources will be helpful:
  • datascienceplus.com
  • kaggle.com
• 
  Develop a research question that is clear and focused, is able to be researched and answered, and that provides an opportunity for analysis rather than description.
  
• Using the examples of a car and a toaster, explain the functional and non-functional requirements of each. (A car’s main function is to carry passengers and luggage between locations safely. A non-functional requirement for a car is its colour, leather seats or top speed.) Explain to students that if a car cannot complete its main function, its other features are not going to make up for this deficiency. Relate this example to data presentation related requirements. If a visualisation does not contain accurate data and is not clear to read, its fonts, dynamic nature and colour schemes (examples of non-functional requirements) will not overcome the issue.
  
• Explore the Information is Beautiful website and document the purpose of, and the different types of data visualisations presented.
  
• Students use the problem-solving methodology and free web-based software Adobe Spark to create a product (image, video, web page) that describes the:
  
  • features of one of the following design principles for appearance in a data visualisation or infographic: alignment, balance, contrast, image use, space, text/table formatting
  • functionality of a data visualisation: ease of use, flexibility, robustness, accessibility, navigation, error tolerance.
  Students create their description (analysis – data requirements), make a quick paper-based sketch of how they want their product to look (design), build the product using Adobe Spark (development), and review the effectiveness of fellow classmates’ work (evaluation).
• Provide a range of datasets to students. Using an online design tool such as rawgraphs students create a design of a visualisation in order to clearly explain the main findings of the data.
  
• As a class, view each of the infographic templates listed on the website: venngage.com. Discuss how each infographic format used improves the clarity of message. Students create a sample infographic on a topic of their choice using one of the templates.
  
• Demonstrate how to construct a rubric for evaluating design ideas, using at least a high/medium/low scale for each area of assessment. Example ways in which a design idea can be evaluated include: ease of creation, ease of update, ability to be used on a range of devices (accessibility), ability to be created in both portrait and landscape formats, and adherence to design principles for appearance. Demonstrate how to create rubric entries for one of the evaluation methods, and give students responsibility to create their own entries for other evaluation methods.
• To understand project planning, students list all assessment tasks that are due in the next week/fortnight/month (which can be used as milestones), and the activities that they will need to complete to prepare for them, both at school and at home. Create a sequence for these tasks in order of urgency, identify dependencies and allocate time for each task. Update all of this information into a Gantt chart so that students can identify the critical path they will need to follow to succeed in their work.
• Use the document at Notifiable Data Breaches Quarterly Statistics Report to identify the kinds of personal information involved in breaches (relate these to the relevant privacy laws for their definition of personal information). Refer to the report’s glossary to match terms and definitions listed for both human error (accidental threats) and malicious or criminal attacks (deliberate threats).

Detailed example

Develop a research question

Lesson 1:

• Teacher and students discuss examples of a research question and the qualities of a good research question:

• ­A good research question is clear and focused, has an appropriate scope, is not too easy or difficult to answer, is researchable and provides an opportunity for analysis rather than description

• View a list of examples.
• Students brainstorm three different topics that they are interested in researching. They should find secondary quantitative and qualitative data that would assist in giving them additional knowledge when researching these topics.
• Students collect data or analysis articles and reports.
• Students summarise in one sentence the topic that has proved most interesting to them.

Lesson 2:

• Read through the Monash method for developing research questions.
• Students write a requirements list for the data that would need to be collected for their topic in the SAT.

Lessons 3 and 4:

• Students should have found their topic and conducted some research. They narrow down their topic to a single issue or debate within the broader topic. 
• Students write their research question. The answer to the question will need to be identified by the student as they complete their project.
• Place students in groups to review other students’ research questions. Teacher provides students with some basic high-medium-low criteria to assist in guiding this process. Examples of criteria that could be given to students are:

• Topic is interesting to student who has chosen it.
• Student has provided a range of secondary quantitative or qualitative information with which to research the answer to the question.
• The question can be answered (Yes/No plus reasons and evidence provided regarding why?/why not?).

• The question has been scoped.
• The question can provide an analytical answer and is not just descriptive – using ‘how’ and ‘why’ statements instead of ‘what’ and ‘describe’.
• As part of this process, students provide feedback but refrain from giving direct advice to other students as to how to improve the questions. This is important when ensuring each student produces their own authentic work for this outcome.

The Monash University web page above contains three different and useful activities:

• understanding the value of scope on a research question
• learning how to narrow down a topic
• understanding how to phrase a question.

After the research question has been generated, students must work out their own data requirements incorporating project constraints and scope. The process outlined above provides some opportunities for some scoping to have already occurred and students can document this.