Unit outline_

# OLET1632: Shark Bites and Other Data Stories

## Overview

This OLE gives students a simple, transferable approach to the exploration of multivariate data in everyday life using simple coding in R. You will investigate the relationship between variables in spreadsheet like data, learning what questions to ask, what techniques to use, and what mistakes to avoid. Focused on concepts, not formulae, the OLE is accessible for students from any discipline. You will focus on studies, including: How does the Australian public respond to shark bites? Is mobile phone usage related to higher incidence of brain tumors?

### Unit details and rules

Academic unit Mathematics and Statistics Academic Operations 2 None None None None Yes

### Teaching staff

Coordinator Jie Yen Yen Fan, jieyen.fan@sydney.edu.au

## Assessment

Type Description Weight Due Length
Assignment MasteryQuiz0
Review of Module0
6.25% Progressive self paced
Outcomes assessed:
Assignment MasteryQuiz1
Review of Module1
6.25% Progressive self paced
Outcomes assessed:
Assignment MasteryQuiz2
Review of Module2
6.25% Progressive self paced
Outcomes assessed:
Assignment MasteryQuiz3
Review of Module3
6.25% Progressive self paced
Outcomes assessed:
Assignment Lab Report (Written)
Synthesis of Modules; Reproducible report [individual/team]
35% Week 02 self paced, due before Lab3
Outcomes assessed:
Assignment Lab Report (Oral)
Presentation of report; Interrogation [individual/team]
20% Week 02 self paced, delivered in Lab3
Outcomes assessed:
Assignment Critical Review (Article)
Critical review of a research article
10% Week 02 self paced, due day after Lab 3
Outcomes assessed:
Assignment Critical Review (Report)
Critical review of peer's reports
10% Week 02 Self paced, due in Lab 3
Outcomes assessed:

### Assessment summary

All MasteryQuizzes and LiveLab Report are submitted online through Canvas. The LiveLab presentation is given in LiveLab3.

### Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

### Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

• Deduction of 5% of the maximum mark for each calendar day after the due date.
• After ten calendar days late, a mark of zero will be awarded.

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

## Learning support

### Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

### Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

### Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

## Weekly schedule

WK Topic Learning activity Learning outcomes
Progressive Introduction to data and R Individual study (2 hr)
Data detective Individual study (1.5 hr)
Data exploration Individual study (4.5 hr)
Data interpretation Individual study (2 hr)
3x Labs Practical (6 hr)

### Attendance and class requirements

Students must attend the Labs, either on Campus (CC) or online on Zoom (RE).

### Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

## Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

• LO1. perform data analysis on multivariate data in a team, and communicate the findings through reproducible statistical reports and oral presentations
• LO2. perform data analysis on multivariate data
• LO3. perform initial data analysis on multivariate data
• LO4. choose appropriate statistical tools for multivariate data
• LO5. recognise common mistakes in interpretation.

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

 GQ1 Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline. GQ2 Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem. GQ3 Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context. GQ4 Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies. GQ5 Inventiveness Generating novel ideas and solutions. GQ6 Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues. GQ7 Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries. GQ8 Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context. GQ9 Influence Engaging others in a process, idea or vision.

### Outcome map

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

## Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes needed.