Motivation and emotion/Assessment

Assessment

Overview

The major project takes a deep dive into a specific topic of interest, while quizzes assess breadth of knowledge.

The major project provides a capstone experience scaffolded into four stages:

This project applies psychological science to real-world problems to produce useful open educational resources.

You can showcase this work in your resume and e-portfolio.

Summary

Item	Weight	Due	Late submissions	Extensions	Description	Time involved (150 hrs)
Topic selection	0%	Week 02 Mon 9am 05 Aug 2024	Not accepted	Not available	Ungraded early assessment exercise. Create a Wikiversity account. Sign up to a major project topic. Ask clarifying questions etc.	1 hour
Topic development	10%	Week 03 Fri 9am 16 Aug 2024	Not accepted	Not available; withdrawal before Census Date recommended	Develop plan for book chapter. Overview. Headings. Key points. Figure. Learning feature. Resources. References. User page. Social contribution.	14 hours: 4 hrs to learn "how" (incl. 2 x 1 hr tutorials), 5 hrs research, 5 hrs preparation
Academic integrity module	0%	Week 11 Mon 9am 07 Oct 2024	Not accepted	Available with documentation	Evidence of completion of the Academic Integrity Module in the current calendar year is required in order to pass the unit.	N/A
Book chapter	45%	Week 11 Mon 9am 07 Oct 2024	Up to 3 days (-10% per day)	Available with documentation	Author an online book chapter up to 4,000 words about a unique motivation or emotion topic. Includes a social contribution component.	60 hours: 15 hrs to learn "how" (incl. 10 x 1 hour tutorials), 18 hrs research, 28 hrs preparation
Multimedia presentation	20%	Week 14 Mon 9am 28 Oct 2024	Up to 3 days (-10% per day)	Available with documentation	Record and share a 3 minute online multimedia presentation focusing on key problems and answers provided by psychological science. Same topic as book chapter.	12 hours: 3 hrs to learn "how", 6 hrs preparation, 3 hrs to record & publish.
Quizzes	25%	1 - Week 04 Mon 9am 19 Aug 2024 2 - Week 06 Mon 9am 02 Sep 2024 3 - Week 08 Mon 9am 16 Sep 2024 4 - Week 11 Mon 9am 07 Oct 2024 5 - Week 13 Mon 9am 21 Oct 2024 6 - Week 15 Mon 9am 04 Nov 2024	Not accepted	Available with documentation	6 equally-weighted 10-item, 10-minute, multiple-choice, online quizzes. One quiz per module. Based on textbook readings.	63 hours: 24 hrs lectures (12 x 2 hrs), 34 hrs reading (17 chs x 2 hrs), 3 hrs completing quizzes (6 x 10 mins)

Requirements

All assessment items are to be submitted online via UCLearn.
Submission of all assessment is optional. Non-submissions will be awarded 0.
Final marks and grades
- It is not necessary to pass each assessment item, however a final mark of 50% or higher is required to Pass the unit.
- The UC grading schema (HD = 85+, DI = 75 to 84, CR = 65 to 74), and P = 50 to 64) will be applied to final marks.

Alterative assessment

The major project (topic development, book chapter, and multimedia presentation) assessment exercises use collaborative, online, public platforms, including Wikiversity.
Use anonymous accounts if you have privacy concerns.
Students own the copyright to their work.
Contributing to Wikiversity requires Creative Commons Sharealike 4.0 licensing of that work.
If this isn't acceptable, negotiate an alternative assessment format which satisfies the learning outcomes and graduate attributes with the unit convener
In the absence of email communication to the unit convener requesting alternative assessment, it is assumed that participation in the standard assessment exercises is willingly undertaken. The onus is upon the student to negotiate alternative assessment.

Nutshell Acknowledge generative artificial intelligence (genAI) use in the edit summary, including tool and prompt details. Fact-check all genAI content and cite peer-reviewed sources. Human-rewrite genAI content to enhance quality.

Summary

GenAI tools can aid but should not replace independent thinking. If using genAI tools for the major project, acknowledge its use in Wikiversity edit summaries. If in doubt, following the principle that "more acknowledgment is better than less". Academia is based on transparency. However, acknowledgement is not required for low-level tasks such as improving spelling and grammar.

You are responsible for content you submit. Be aware of limitations of genAI tools such as biases and inaccuracies. GenAI tools work best for topics you already understand, with carefully crafted prompting based on reading of peer-reviewed literature. Refine prompts for better results. Fact-check generated content and provide appropriate, peer-reviewed citations. GenAI content should also be human-revised/rewritten in order to improve it. For example, genAI content is often overly verbose. Despite the risks, genAI tools can aid in brainstorming, explaining key concepts, synthesising ideas, developing examples, and improving the readability and quality of written expression.

If you are unsure about appropriate use, ask questions and discuss, so we can all learn together.

Detailed guidelines

Use ethically, with caution

Learning to use generative artificial intelligence (genAI) tools (such as ChatGPT, Claude, and Gemini) responsibly and ethically is an emerging skill. GenAI tools can be used to enhance academic work, but should be used judiciously and as a supplementary tool, rather than as a replacement for independent thinking and academic inquiry.

How to acknowledge

GenAI tools may be used to assist in preparation of the major project (topic development, book chapter, multimedia presentation). Use of such tools must be clearly acknowledged in Wikiversity edit summaries (see Figure 2), otherwise it is a violation of academic integrity.

Best practice is to include a publicly accessible link to the chatbot conversation. ChatGPT shared links FAQ). If a link can't be shared, then include sufficient details about the prompt in edit summary, (e.g., "ChatGPT May 24 Version. Prompt detail or summary") (see Figure 3). The chatbot conversation should not be included as a citation and reference because it is not a reliable, primary, peer-reviewed source.

These practices help to ensure that the use of genAI is clear and transparent and that genAI material has been human-checked and verified. Transparency is key to good practice in academia and professionalism. If in doubt, err on the side of providing too much acknowledgement detail rather than not enough. However, there is no need to acknowledge genAI use for low-level tasks such as fixing grammar and spelling.

Limitations

Be aware of the limits of genAI tools. Content they generate may be inaccurate, biased, incomplete, or otherwise problematic. Low user-knowledge and minimal effort prompting tends to yield low quality results. Refine prompts to get better outcomes. You are entirely responsible for the accuracy and quality of any content you submit. Always fact check.

Fact-check and cite

Regardless of whether genAI has been used, all claims need to be supported by verified peer-reviewed citations which you have directly consulted. Thus, whilst genAI acknowledgement is necessary, where it has been used, it is not in and of itself a sufficient basis for supporting claims. The author must do independent reading and checking to identify appropriate peer-reviewed citations to support any claims being made. Low-energy or unreflective reuse of text generated by genAI without further investigation and reviewing of primary, peer-reviewed academic literature will lead to poor quality results. GenAI tools work best for topics which you already understand. Guide and craft genAI responses based on your reading of peer-reviewed theory and research.

Going forth

Despite these warnings, you are encouraged to explore use of genAI tools to help develop higher quality work. Recommended uses of genAI tools include:

brainstorming
explaining key concepts
developing a structure
synthesising complex ideas
readability analysis
improving the quality of written expression
checking spelling and grammar
image generation (e.g., see Figure 1)
requesting critical feedback and suggestions for improvement

If you are unsure about how to use genAI effectively or how to acknowledge its use appropriately, ask questions and discuss, so we can all learn together.

Learn about genAI

WTF is AI? provides a useful introduction to genAI and a non-technical overview about how genAI works, what it is capable of, limitations, and issues.

To learn more, explore GenAI for students (University of Canberra Library).

See also

Wikibooks:Artificial Intelligence (draft proposal)
Wikipedia:Large language models (draft proposal)
Wikiversity:Artificial intelligence (draft proposal)

Extensions

Extensions for the Topic Selection exercises are not available. Students unable to submit these assessment item by the due date should withdraw prior to the Census Date.
Extensions for other assessment exercises will only be granted in exceptional circumstances. Progress on the assessment items is expected throughout the teaching period. Early communication of problems is strongly advised.
Extensions will not be granted for:
1. Workload (e.g., study load and/or paid or voluntary work)—such problems should be anticipated
2. Technical problems (e.g., lost/corrupted/damaged storage media, software/internet access problems, and viruses)—keep multiple and regular backups
3. Undocumented issues
Submit extension requests via the online "Extension application form" available in the UCLearn site. Include documentary evidence that covers the length of requested extension.
Submit a separate request for each assessment item for which extension is sought
The unit convener will consider the request and advise the outcome. If approved, the new due date will appear in UCLearn.
For further information about extension requests, see:
1. Assessment Policy section 3.19
2. Assessment Procedure section 3.167–3.191 on Extenuating Circumstances (Deferred examinations and extensions).
3. Assignment extensions

Late penalty

No late submissions for the topic selection are accepted.
Other assessment items can be submitted up to 3 days late without an approved extension. This will incur a 10% penalty per day (i.e., -10% of total marks available for the assessment item), including weekends. A part-day late is counted as a full day late. If submitted beyond 3 days late, 0 will be awarded for the assessment item.

Marking and feedback

Assessment will generally be marked and feedback provided within three weeks of submission.
Availability of marks and feedback will be notified via the unit's UCLearn Announcements.
Assessment submitted after the due date and time, regardless of whether an extension was granted, may be returned at a later date than those submitted on time.
Late submission may result in reduced feedback being provided.