代写COMM5501 Data Story Project Guide代写数据结构语言程序
- 首页 >> Java编程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.
0 Engagement and Participation
A large component of success in this course is to engage with your peers for feedback. The labs/tutorials in particular are critical to making sure you are on the right track. Based on feedback from previous students, we are reinforcing this messaging by including a lab participation component to this course.
There are 9 labs in total. Students start with 1%, and will receive an additional 2% for participation in each lab, up to a maximum of 7 labs (i.e. a max total of 15%). We of course strongly recommend that students attend all labs, but we also understand that sometimes unforeseen circumstances prevent you from attending all classes.
If you are unable to attend your regular lab in one week, you may attend a different one for that week, but you will need to let the tutor know.
The first component (excluding component 0 above) 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 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 corre- sponding lab 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 200 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).
11:59pm Sunday of Week 3.
1.5 What makes a good submission?
N/A, the first component is formative in nature. However, to maximise the benefit from this activity, students will need to incorporate feedback they’ve already received and make a genuine effort to present their best Impact Statement. Students will then receive some additional feedback from the teaching team for this work.
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 5% 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 lab in week 4 will have an activity to help you start on the Thank You Team activity (Section 2.3.2).The formative forum post in week 3 will give students another opportunity to get additional feedback from other students.
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.
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
The activity for completing the form. will be completed in the week 5 lab. 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
• GyG: 11:59pm Sunday of week 4. You will need this to complete the Tyt activity in the week 5 lab.
• Tyt activity: Week 5 lab
• Tyt follow-up post: 11:59pm Sunday of week 5.
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.
The third component of the Data Story Project will explore an opposing perspective to that chosen by the student. It is very easy to find information supporting any given perspective (confirmation bias), so students will need to genuinely consider this opposing perspective, assess its merits, and create an appropriate counter-argument. Students will need to find a source that opposes their chosen stance, analyse this stakeholder using the framework from Topic 4, and provide an evidence-based assessment of the validity of this source. The focus of this component is to use data to provide the counter-argument.
The purpose of this task is for students to show versatility by working outside their own boundaries and considering an opposing perspective. There will again be opportunities to receive feedback from peers and from a tutor.
Please note that this component is a summative task and will be graded by tutors. This component is has a 20% weighting towards your final grade
The lab in week 5 will contain guided activities to help students nominate and analyse the opposing perspective, including identifying the corresponding stakeholder that might hold this opposing per- spective. The stakeholder framework from Topic 4 can then be used to understand this stakeholder further, so that a response can be planned.
As with previous components, there will also be formative discussion forum posts for getting further peer feedback.
Students will submit a single PDF document to Turnitin containing their finalised work from week 5. This document will need to include:
• Your topic question/statement and summary of the challenge for reference. This will not be
marked, but will give the marker the appropriate context for this deliverable. This can be the same as (or very similar to) what you submitted in the first deliverable.
• Citing the chosen opposing perspective (for example, a link would suffice), and the likely stake- holder with this view.
• A brief summary of the opposing perspective. (max 200 words)
• Any relevant data visualisations used in the opposing perspective (clearly label and caption these as appropriate).
• Data visualisations created by the student that form. the bulk of the counter-argument.
• Accompanying written notes to give context to student’s data visualisations and express the overall approach of the counter-argument. (max 200 words)
NOTE: Whilst the stakeholder framework from Topic 4 is not an explicit part of the deliverables for this component, it is highly recommended that you use it to help structure your counter-argument.
11:59pm Wednesday week 7. We have brought the due date back from the usual Sunday to try and
allow the teaching team to begin marking earlier.
The teaching team will try to do our best to provide feedback in a timely manner. Unfortunately, we cannot guarantee a 1-week turnaround. However, there will still be peer feedback from the corre- sponding formative assessment, as well as informal feedback from your tutor to help you refine this third component for use in the fourth (and fifth) component(s).