COMM5501 Data Story Project Guide
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Data Story Project Guide
COMM5501
Introduction
The major project for COMM5501 is structured to provide students a step-by-step guide to building their own data story on a topic of their own choosing, related to the UN Sustainable Development Goals (SDGs). A link to the SDGs is included HERE for your convenience.
Students will need to select a contemporary challenge related to the SDGs, find the relevant data, process and present this data in an insightful and coherent manner, and apply their own judgement based on their findings to give an evidence-based recommendation to the identified challenge.
Whilst there is a “Data Story Content” assessment and a “Data Story Project” assessment as part of this course, we will use the term “Data Story Project” to refer to the overall process of creating your data story.
The first three components of the Data Story Project will focus on building content for your data story. The fourth component will combine the content from the first three into the final version of the data story, and students will present their collated work in an appropriate format (guidance will be provided). The fifth component will require students to showcase their work as part of their profes- sional portfolio.
This Data Story Project has a total weighting of 80% of your final grade for this course. The 5 com- ponents mentioned above will be submitted throughout the term. The key details for each component are provided below.
Please note that this document is only a guide for what to expect, as we may make changes during the term to respond to unforeseen circumstances. This document should not be seen as being set in stone.
1 Choosing your Challenge
1.1 Description
The first component of the Data Story Project will introduce students into this task by proposing a topic that is both of interest to them and has a meaningful impact to the broader society. The chosen topic will need to connect to at least one if the UN SDGs.
For this chosen topic students will need to provide:
. An Impact Statement explaining why that proposed topic is important, both in general and to them individually, and
. Identify a relevant data set from a reputable source that can support this topic.
This will serve as a starting point for subsequent components of the Data Story Project.
The purpose of this submission (in particular, proposing three topics) is to receive feedback from a member of the teaching team as well (students would already have received peer feedback before submitting).
NOTE: Students are NOT locked into this topic for the final version of their data story, and are allowed to adjust their topic statement/question as they progress through the semester.
This component is has a 5% weighting towards your final grade.
Topic 1 will contain various activities to support students in exploring the SDGs broadly. The lecture for Topic 2 will provide an introduction into writing an effective Impact Statement, and the correspond- ing workshop in week 2 will contain aguided activity for students to write their own Impact Statement.
Students will also post a copy of this component for their formative forum post for week 2, where they will receive peer feedback. Students are encouraged to take any additional feedback they receive here into consideration before submitting the deliverable for this task.
Students will gather all the feedback they’ve received and make any changes they feel are necessary, then post an updated version of their work to the Deliverables section on Moodle to receive feedback
from the teaching team. Your post will need to contain the following:
. A single-sentence topic statement/question,
. A corresponding Impact Statement (max 150 words),
. A link to the chosen data set, a brief description of the data set, and a proposal for how the chosen data set might be used to support the Impact Statement (max 50 words).
Early week 3 to receive feedback from a tutor. If you submit later during week 3, there’s a chance the tutor has already completed giving feedback and you may miss out.
Aim to submit your work by Friday 11:59PM in week 3 at the latest. Any submissions made after Friday 11:59PM in week 4 will likely not receive feedback.
1.5 What makes a good submission?
N/A, the first component is formative in nature and it is assumed that students have already incor- porated feedback they’ve received. Students will receive some additional feedback from the teaching team for their work.
2.1 Description
The second component of the Data Story Project will take the ideas of “good” and “bad” data visu- alisations and apply them to their chosen topic.
Students will take the data set they chose from the “Choosing your Challenge” component (or another data set if necessary) and document their process of improving their first chart, the types of feedback they received, and how they implemented this feedback.
This task serves multiple purposes:
. Documenting the process with clear notes creates a reusable resource for referring back to the process you used to create your graph.
. It reduces the chance of repeating the same mistakes and speeds up the process for creating your subsequent graphs.
. More broadly, it reinforces the learning process. You are very likely learning a relatively new skill, and it’s very easy to forget a detail if you don’t write it down (this is still true if you’re refining an existing skill).
You will receive peer feedback throughout this process. This component will have very limited tutor feedback.
This component is has a 15% weighting towards your final grade.
The lab in week 3 will contain guided activities to help students build effective data visualisations, and receive peer feedback on their work before submitting. The formative forum post in week 3 will give students an opportunity to get additional feedback from other students. The lab in week 4 will also have an activity to help you start on the Thank You deliverable (Section 2.3.2).
Some of the elements from Topic 4 on stakeholders may also be relevant for this component, as a large portion of understanding the purpose of a graph comes from understanding the target audience as a stakeholder.
There are 2 sets of deliverables for this second component.
2.3.1 Growing my Graph
Students will submit a single PDF document to Turnitin containing the main iterations of their graph supporting the Impact Statement. You do not need to include every single version of your graph, just key checkpoints and major changes.
This document also needs to have brief notes on the changes made between each iteration. These notes should contain not only the change being made, but should also mention the rationale behind the change (i.e. Why did you make that change?). The notes can be dot points, but you can also have more text if you feel this is necessary.
These notes should be detailed enough to be a convenient reference material for yourself later in the term. A sample has been provided on Moodle for what this may look like.
The deliverable for this task is a completed ”Thank you team!” form (available on Moodle) and a follow-up Moodle post.
The activity for completing the form will be completed in the week 5 lab and the form will be collected by your tutor. Details for this task can be found in the week 4 and week 5 lab activities document, as well as the form provided on Moodle.
After the activity, students will need to make a follow-up post in the Deliverables section on Moodle to summarise the key parts of your presentation. A sample has been provided on Moodle for what this may look like.
This activity will be conducted in the week 5 labs.
2.5 What makes a good submission?
The purpose of deliverable 2.3.1 is to create a record that you can revisit when making subsequent data visualisations to speed up that process and make better visualisations. The information below is a more detailed guide for making a better set of resources for yourself (traditionally, this might sometimes be called a “marking rubric”).
2.5.1 Good and Bad Visualisations
What separates a poor from a useful resource lies in the notes. It’s entirely possible that you created a very good graph in your first attempt and you didn’t need to make many changes. If there were elements of your first draft that were good that you deliberately kept the same, document your reasons for doing that as well.
Poor |
There is little documentation explaining the process of iterating the graph. At best, the documentation is largely declarative, e.g. “colour scheme changed from bright red to dark blue”, instead of explanatory, e.g. “The bright red colour scheme was uncomfortable to look at, so I changed to dark blue. Much more comfortable” . |
Acceptable |
Changes (or lack thereof) are largely driven by the visualisation prin- ciples covered in class (e.g. Gestalt or Tufte’s principles), and this is documented. |
Excellent |
Changes are driven primarily by the underlying purpose of the graph: the message being conveyed and the target audience. The visualisation principles covered in class are also considered, but these are secondary concerns. |
The “formal” term for this deliverable is a “reflection”, and should be based on the Gibbs’ Reflective Learning Cycle. For example, if you want to reflect on a particular piece of feedback (e.g. your group did not see the pattern you intended in your graph), then run through the six steps. Use the table below to help you write your reflection, then again to help you make a self-assessment.
Criteria |
Poor |
Acceptable |
Excellent |
|
Use of reflective process |
The reflection does not seem to engage with the reflective cycle at all or only minimally, missing a number of elements or misunderstanding their purpose. There does not appear to be any genuine attempt to en- gage with feedback re- ceived. |
The reflection covers most of Gibbs’ Reflec- tive Cycle adequately, perhaps lacking com- prehensive coverage in some places. At least attempts to genuinely engage with the reflec- tive process linked to the feedback received. |
Covers all aspects of Gibbs’ Reflective Cycle to an appropriate de- gree, showing genuine engagement with there- flective process directly linked to the feedback received. |
|
Areas of Improvement |
Does not highlight any areas for improvement, perhaps believing there are no gaps or opportu- nities to grow. |
Highlights some ar- eas for improvement, whether self-assessed or noted by teammates, and provides limited reasoning as to why these gaps occurred. Reflects on what else could have been done, but this maybe limited, perfunctory, or not par- ticularly relevant. |
Shows strong self- awareness by high- lighting areas for improvement, whether self-assessed or noted by teammates, and provides clear reasoning as to why these gaps occurred. Displays a growth mindset by
2023-09-23 |