Feedback
Feedback will be provided using Canvas’ Speed Grader
and may include written inline feedback, general comments and/or audio feedback.
Submission Checklist
- A web-hosted (e.g. Plotly, RPub, Shinyapps.io, Youtube) data visualisation/app/video/slideshow etc. (see below)
- You must include a reference to your data source(s) in the online assignment!
- A PDF or Word document containing your student name, student number, URL to your published assignment and all the code used for Assignment 3. This document does not need to be knitted.
Questions about Assignment 3? Ask on Slack (Links to an external site.).
Failure to meet the submission requirements will incur a late penalty and delay your feedback.
Assignment Instructions
The goal of Assignment 3 is simple. Tell a compelling story using data visualisation. What story you tell and how you tell it is up to you. The only constraints are detailed below.
Constraints
- Assignment 3 must be based on open or public data. Ensure you include a reference to your data source(s) in the assignment.
- Create ONE of the following: video, dashboard, application, slideshow/storyboard, interactive plot OR a static plot. Reports are NOT permitted. How you tell the story is up to you. However, the following restrictions apply:
- Videos and animation: 3 minutes maximum
- Dashboards/apps/single plots/faceted plots/multiple plots etc: One HD screen (1080p)
- Slideshow/storyboards: 10 transitions/slides
- Your assignment must be hosted online and publically accessible using a URL (e.g. Plotly, RPubs, Shinyapps.io, YouTube etc.)
Late Submissions
Late submissions will be marked in accordance with the late submission policy. Please see the Welcome and Orientation Module in Canvas.
Collaboration versus Collusion and Plagiarism
You are permitted to discuss and collaborate on the assignment with your classmates. However, the assignment must be an individual effort. Assignments will be submitted through Turnitin, so if you’ve copied material, code and data from the web or from a fellow classmate or previous students, it will be detected. It is your responsibility to ensure you do not copy or do not allow another classmate to copy your work. If plagiarism is detected, both the copier and the student copied from will be responsible. It is good practice to never share assignment files with other students. You should ensure you understand your responsibilities by reading the RMIT University website on academic integrity (Links to an external site.). Ignorance is no excuse.
Marking Rubric
See below.
Rubric
Open Data Your Turn
| Open Data Your Turn | ||
| Criteria | Ratings | Pts |
| This criterion is linked to a learning outcomeNarrative and Engagement This is a rating of the extent to which the final assignment presents a compelling and memorable narrative visualisation. This criterion takes into account how well the narrative engages the audience, how well the story pieces are ordered, and the clarity of the story’s objective. This rating also reflects the appropriateness, variety and effectiveness of the narrative data visualisation strategies employed. | 10.0 to >7.5 Pts Excellent 7.5 to >5.0 Pts Good 5.0 to >0.0 Pts Needs Improvement 0.0 Pts Not acceptable | 10.0 pts |
| This criterion is linked to a learning outcomeData This is a rating of the quality of the data sourced, processed and visualised for the assignment. This criterion considers the relevancy, recency, and reliability of the data selected. It also considers the opportunities to present other data/sources that would significantly improve the overall narrative. | 5.0 to >4.0 Pts Excellent 4.0 to >2.0 Pts Good 2.0 to >1.0 Pts Needs Improvement 1.0 to >0 Pts Not acceptable | 5.0 pts |
| This criterion is linked to a learning outcomeVisual Techniques This is a rating of the overall quality and demonstrated mastery of the data visualisation techniques used in the assignment. This criterion takes into account the appropriateness of the methods and techniques used to visualise the data and support the overall narrative. It considers the extent to which the student has demonstrated good data visualisation practice covered throughout the course and technical mastery of open-source data visualisation packages. | 10.0 to >7.5 Pts Excellent 7.5 to >5.0 Pts Good 5.0 to >2.5 Pts Needs improvement 2.5 to >0 Pts Not acceptable | 10.0 pts |
| This criterion is linked to a learning outcomeChecklist/Requirements This is a rating of the quality of the assignment submission. It takes into account whether a student has submitted the required items listed in the submission checklist, followed all the instructions, and demonstrated academic integrity and professionalism. This includes proper referencing. | 5.0 to >4.0 Pts Excellent 4.0 to >2.0 Pts Good 2.0 to >1.0 Pts Needs Improvement 1.0 to >0 Pts Not acceptable | 5.0 pts |
| Total points: 30.0 |
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