Academic Honesty

Presenting the work of another person as one’s own is a serious breach of the University’s rules and carries significant penalties. In this unit, we will be checking written work for plagiarism using Turnitin. Penalties for plagiarism may include a zero mark for the assignment or in more extreme cases, failure of the unit. Plagiarism WILL be noted on your academic record. (link: Academic Integrity Policy)

Late Assessment Submission Penalty

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern.  For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration. 

Assessments where late submissions will be accepted 

In this unit, late submissions will accepted for written work. See marking guidance on iLearn or consult the convenor for additional information. 

Special consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please contact the convenor prior to submitting a Special Consideration.

Preliminary analyses

You will complete preliminary analyses for your research project and submit a short summary of your data, methods, and results. This provides an opportunity to get feedback prior to completing your research project, final project report, and presentation.

Project presentation

You will give a brief but substantial oral presentation to your peers in class on the results of your research project. Slides illustrating your data, methods, and results should be used.

Project report

You will write up the results of your research project in the form of a standard-length scientific paper. If possible, it should be divided into the usual sections: Introduction, Data, Methods, Results, Discussion, and References sections. It should be based on using statistical methods and coding skills learned in the class. It should also include several substantially different kinds of graphs.

Software installation

You will need to install the applications R and RStudio on your computer. Please install them prior to the first lecture. If you require assistance contact the convenor to schedule an appointment.

Workshops

There will be two eight-hour workshops early in the semester. You are very strongly encouraged to attend in person unless you are absolutely unable to do so for logistical reasons. You will need to bring your laptop to all of the workshops including these ones.

Practicals

Practicals will last two hours and will be conducted on campus. You will learn much more if you attend in person so I can work with you more efficiently on a one-to-one basis. Make sure to bring your laptop each week.

iLearn

iLearn will be used to provide the lecture schedule and lecture PDFs, to post data files and R scripts, and for me to post announcements.

Logging in to iLearn

• The iLearn login page is: https://ilearn.mq.edu.au/
• Username: your student number
• Problems? Please contact Student IT Help
• Need extra help due to a disability/health condition? Please visit the Student Services Website : https://students.mq.edu.au/support/wellbeing

Missed workshops and practicals

Please make direct arrangements with the convenor if you are unable to attend individual workshops.

Overall grades

The University grading categories are fail (F <50%), pass (P 50%-64%), credit (CR 65%-74%), distinction (D 75%-84%), and high distinction (HD 85%-100%).

Assignment submission, plagiarism, and artificial intelligence

All written assessments will be submitted through iLearn via a Turnitin link. Your written assignment will be checked for plagiarism using Turnitin. Plagiarism will not be tolerated.

Submissions in this unit might in principle be prepared using artificial intelligence (AI). AI is not an effective tool for learning to understand code and to code yourself. Based on past experience, you would spend more time debugging AI-generated R code than you would have spent simply by following the instructions and writing it yourself. AI also frequently misidentifies the statistical methods you are actually trying to learn and use. Similarly, preparing text with AI is not an efficient means of learning to prepare presentations or to write.

Do not under any circumstances lend your work to another student. If that student plagiarises your work, you too may be liable. The penalties imposed by the University for plagiarism are serious and may include expulsion from the University.

A full outline of the University's policy on plagiarism is found at http://www.mq.edu.au/policy/doc s/academic_honesty/policy.html.

Resources and support

How to find the answers:

1. Please read the unit outline.

2. Write directly to the convenor or arrange in an-person meeting.

3. Consult iLearn - your question may have already been asked and answered by another student.

4. If and only if the answer to a question will benefit others, please post it on iLearn.

5. Unexpected adjustments made during the unit will announced via announcements so make sure you check iLearn regularly.

E-mail

Always submit questions directly to john.alroy@mq.edu.au. Please be courteous and patient - I will endeavour to reply to your email quickly.

Text book

There are no required textbooks for the unit because it is hands-on.

In accord with University policy, there are now three assessments.