Digital Analytics (COMU3120)

In this assessment, you will examine a real-world example of unconscious digital analytics that directly impacts you by producing slides and a recorded video. ‘Unconscious digital analytics’ refers to data collection and processing that happens automatically, often without our full awareness. This could involve, for example, productivity metrics, customer service tracking, or digital monitoring and surveillance by organizations such as Microsoft, Woolworths, Amazon, or the Australian Government.

Audience Perspective: As you prepare your slides and recorded video, imagine that you are presenting your findings to a panel of professionals keen to acquire a basic understanding of unconscious digital analytics.

1. Choose a single example of unconscious digital analytics that appears in your own life. Do not select examples of consciously tracked analytics (e.g. menstrual tracking) or active engagements (e.g. posting on social media). Focus instead on data collected and processed automatically, in ways that might go unnoticed. Your example should be as specific as possible. For example, 'email tracking' is too broad; 'Realestate.com.au's email click-through tracking' is more appropriate.

2. Create a 7-slide slide deck critiquing your chosen example. Your slide deck must include the following sections (in any order):

• Title/cover slide: Give your presentation a descriptive title. For example, 'Assessment 1' is not a good title; 'Realestate.com's Use of Email Click-Through Tracking to Monitor Home Buyers' is much better.
• Introduction to your example: Briefly introduce your example and its context.
• Variables in your example: List the variables that your example includes. Note how these variables are operationalised and analysed.
• Benefits: Identify some of the potential benefits of your example. Why might your example be considered positive?
• Issues: Identify some of the potential issues with your example. Why might your example be considered negative?
• Conclusions: Summarise your critique. Is your example ultimately positive, negative, or mixed? Tell us why we should care about your example and insights.
• References: List all resources cited in your slide deck, following APA style.

3. Record a video presentation (no more than 5 minutes) explaining and expanding upon the content in your slides. Use this video to give additional context to your slides, rather than simply reading from them. Record using your own voice; automated voice recordings are not allowed. You do not need to show yourself in the video if you do not want to. Briefly show your 'References' slide; you do not need to cover it extensively in your video.

You are being evaluated on your (1) critique of emerging approaches to the collection and analysis of digital data and (2) ability to compellingly communicate your ideas in a professional style. Any unoriginal resources used must cited in APA style in the 'References' slide. There is no minimum or maximum word count for this assessment, but as a general rule if you are exceeding 500 words (excluding references) you are not making the most of the visual opportunities afforded by a slide deck. The visual experience of your slide deck must contribute to audience understanding of your critique.

Your submission must include references to at least two academic sources, and at least one official news source (e.g. ABC News, The Conversation, BBC News), for a minimum total of three sources. These references must be integrated through in-text citations, and must adhere to APA style. You must reference any unoriginal assets used. If you are working with personal data, please ensure that any sensitive data is not included in your submission (e.g. blacking out or removing such text).

You must submit both your video and slides.

Artificial Intelligence: Assignments for this courseﾠevaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

You have been asked to complete a variety of tasks in the weekly tutorials. In this assessment, you will identify a single instance in one of these tasks when something did not go right, or when you struggled to understand or do something. Maybe you got an ‘error’ message. Maybe something was formatted incorrectly. Maybe you just didn’t know what to do next. Whatever instance you choose, write a reflection on this experience in 750 words (+/- 10%), excluding references.

Your reflection must include:

1. Introduction to the tutorial task: Briefly explain what the task involved and what you needed to do.
2. Description of the issue you faced: Describe a single instance of what went wrong. If you understand the reason for the issue now, explain why it happened.
3. Actions taken to resolve the issue: Describe how you attempted to solve the problem. If you couldn’t resolve it, explain what you did instead and why.
4. What you learned: Explain what this experience taught you about digital analytics. How does this experience connect with the concepts (e.g. computational thinking) and content we have covered in the course? This section is weighted the most strongly, so it should be your primary focus.
5. Conclusion: Briefly summarise the main points of your reflection.

You are being evaluated on your (1) ability to identify and solve analytical and coding issues self-sufficiently and (2) situate your own experiences within broader class discussions of digital analytics. You may find the concept of computational thinking especially useful for this assessment.

You are encouraged to use the first-person ‘I’ pronoun in this assessment.

If images help you more clearly articulate your ideas, you are welcome (but not required) to include them; if included, you must explicitly introduce, explain, and respond to these images in the body of your text, and they must include figure labels (e.g. 'Figure 1') and explanatory captions (neither included in the word count).

Your submission must include references to at least two academic sources. These references must be integrated through in-text citations, and must adhere to APA style.

Artificial Intelligence: Assignments for this courseﾠevaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

For this assessment, you’ll work with a dataset of your choice (options provided on the course Learn page) to identify key insights, which you’ll communicate in a variety of ways. This assessment has three parts.

1. A thesis sentence (no more than 75 words) summarising why we should care about your dataset. Begin by examining your dataset. Think about the kinds of questions it raises and any key insights you find. Based on this examination, answer the question ‘Why should we care about your dataset?’ in a single sentence of no more than 75 words. This sentence is your thesis statement, and it must make a specific argument about your dataset.
2. Two visualisations made using R. Use R to create two visualisations that highlight the significance of your dataset. Each visualisation should include a brief explanation (no more than 150 words) on how it supports your thesis statement. You must submit the code used to generate both of these visualisations; this code must include comments (using # throughout the code) that show your understanding of each step.
3. Visualisation 1 must be a bar chart made using the barplot() function used in tutorials. Your bar chart must include: data; a title; axis, variable, and range labels; reasonable scale(s); and a data source.
4. Visualisation 2 will be in a form of your choosing. You must choose an appropriate type of visualisation for your data and thesis. This visualisation must demonstrate R proficiency and good practice for data visualisations as we have reviewed in class. To make this visualisation, you will need to self-direct your learning using some of the many available R resources. You cannot use the barplot() function a second time, but you can make another bar plot using a different function or package (e.g. ggplot2) if you think this is appropriate.
5. A one-page (A4 size) proposal for a creative data presentation. Imagine another way to present your data using digital technology. This presentation must help others understand its significance as per your thesis statement. Imagine that your audience is a group of enthusiastic-but-easily-bored undergraduate students wanting to learn more about data and digital analytics.

Describe your idea for a creative digital presentation of your data on a single A4 page. Identify what data – and additional data, if needed – you would use, and how your presentation would help others understand your dataset and why they should care about it. Your understanding of course material should be implicit throughout your proposal.

You may include text, images, or a combination. There is no minimum or maximum word count for this part of the assessment, but everything must fit onto one A4 page.

You can be as aspirational as you wish in this proposal. Your idea must be feasible, but you do not need to execute it.

Across its three parts, your assessment submission must include references to at least two academic sources, at least one 'training' source (i.e. a source that you used in your self-directed learning), and at least one popular source (e.g. blog, YouTube video, museum catalogue), for a minimum total of four sources. These references must be integrated through in-text citations, and must adhere to APA style. You must reference any unoriginal assets, tutorials, and/or training modules used.

Submission Requirements

Your submission must include:

• Your thesis statement.
• Two visualisations made using R, one of which has been made using the barplot() function and the other in a form form of your choosing. Each of these visualisations must include brief explanations (no more than 150 words each) of how they support your thesis statement.
• The R code used to generate both of your visualisations. This code must include comments (using # throughout the code) that explain each step.
• Your one-page (A4 size) proposal for a creative data presentation.
• A references list.

Artificial Intelligence: Assignments for this courseﾠevaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.