Microsoft Word – A3_instructions_and_rubrics_20210914.docx
COMM2501/5501 Assessment 3, T3 1
Assessment 3: Visualisation Portfolio Blog
Assessment instructions and rubric
Task
This iterative portfolio task will encourage you to build a professional portfolio of data visualisations using
storytelling methods critical to contemporary data visualisation practice. You will explore a chosen data set
using analytical techniques learned from the labs to produce data insights. Over the term you will continue to
refine your visualisations into a data story, which will be presented in a professional blog format.
In this assessment you will gradually build a data story using techniques in data analysis and visualisation that
you will learn through the term. This is a progressive portfolio task that you will complete over weeks 1 to 10.
By the completion of the assessment you should have a professional portfolio to present to colleagues and
potential employers.
This is a thematic task, in which you will be assessed for your ability to create a compelling data story. You will
be asked to choose your theme and dataset by the end of week 4 (more details will be provided in week 4
formative activity).
Theme selection is as follows:
• You may use the provided theme and data sets suggested on the A3: Datasets examples page on Moodle
in Assessments section.
• Or, you may choose a theme and data set of your own devising, provided that you have chosen and
explored the dataset by the end of week 4 (you need to make sure that the dataset you want to choose is
not too complicated/dirty; you are responsible for the cleaning of the dataset; you should not
underestimate the amount of extra work it could be to deal with messy data).
During modules 1-5, you should apply analytical and visual techniques learned from the labs and UX design
modules to progressively explore your dataset. It is important that you post your weekly progress on your blog,
as this will assist you to gain insight into the development of your ideas and data story. You may want to revisit
certain data using new techniques learned in later weeks. Similarly, your data story may not reveal itself until
you have spent a considerable amount of time analysing data through numerous techniques.
Instructions
1. Your final assessment must be presented on a website (created with R Markdown) as a series of
visualisations (you will learn how to create and publish a blogdown with R in week 5 lab). A penalty will be
applied if your assessment is not presented on a website as per the instructions.
2. The visualisations describe a data story on your chosen theme.
3. There is no set number of visualisations you must use, but you must employ enough methods to
compellingly illustrate your data story.
4. You may use any analytical methods available to you but must be guided by the principles of data story
telling.
5. You must cite your sources, use your own words and/or make explicit what is not your own work (e.g. you
can include images if this could support your work but cite the sources).
6. This is individual work.
See also the assessment rubric below.
COMM2501/5501 Assessment 3, T3 2
Supporting resources
The successful completion of this assessment task is supported by the required weekly training in R and
Tableau labs. This assessment evaluates your understanding of these platforms and your competency with
them. The required labs provide comprehensive training necessary to complete the task. You can choose to
only use R or Tableau to create your data visualisations. If you choose to use Tableau for your data
visualisations, you will have to do extra research in order to incorporate your Tableau data visualisations in
your blog (recall that the blog has to be created with R Markdown).
The following (non-exhaustive) listed supporting activities provide direct training for the production of data
visualisations. Completion of these supporting activities will comprehensively prepare you for the task.
Supporting activities:
1. R labs, Supporting activity
2. Tableau labs, Supporting activity
3. Wireframe tutorial, module 4 Supporting activity
4. UX design tutorials, modules 2-3
Submission guidelines
Submit your assessment via the Turnitin link as per the submission template available on the Moodle course
page in Assessments section. See below more information on the Turnitin submission in page 4.
Workload
1-2 hours per week (up to a total of 20 hours)
Assessment criteria
This assignment will be assessed on the following guidelines:
• Data analysis: data description, data interrogation and methodological curiosity
• Data storytelling: ability to weave a narrative based on data
• Design: effectiveness, simplicity and useability of visualisation to convey message
Assessment rubrics
See rubric below.
COMM2501/5501 Assessment 3, T3 3
Fail Pass Credit Distinction High Distinction
Data analysis:
data description
and data
interrogation
(25%)
There is poor
application of
analysis methods
and/or
misinterpretation
of the data; no or
poor description of
the data.
There is limited
application of data
analysis methods;
limited description of
the data.
There is appropriate
application of data
analysis methods;
appropriate
description of the
data.
There is appropriate
and extended
application of data
analysis methods;
appropriate and
extended
description of the
data.
There is appropriate
and extensive
application of data
analysis methods;
appropriate and
extensive
description of the
data.
Data
storytelling:
ability to weave
a narrative
based on data
(25%)
There is little or no
relationship
between data
visualisations,
insights and the
data story. There
is no or poor ability
to weave a
narrative based on
data.
There is some
relationship between
data visualisations,
insights, and the
data story. There is
some ability to
weave a narrative
based on data.
There is a solid
relationship between
data visualisations,
insights, and the
data story. The
visualisations
demonstrate
progression of
ideas. There is solid
ability to weave a
narrative based on
data.
There is strong
relationship between
data visualisation,
insights, and the
data story. The
visualisations
demonstrate a
progression of ideas
and methodologies
that assist
knowledge
formation. There is
strong ability to
weave a narrative
based on data.
There is an inspired
and novel
relationship between
data visualisation,
insights and the
data story. The
visualisations
demonstrate
progression of ideas
and methodologies
that assist
knowledge
formation. There is
excellent ability to
weave a narrative
based on data.
Design of data
visualisation:
effectiveness,
simplicity and
usability of
visualisation to
convey
message (25%)
The design of the
data visualisation
is ineffective,
complex and/or
unusable in
conveying the
message.
The design of the
data visualisation is
described clearly,
but there is little
evidence of clarity
and/or usability in
design.
The principles of UX
design of the data
visualisation are
identified with some
evidence provided.
The message is
conveyed simply
and useably.
The principles of UX
data design
visualisation are
logically and
effectively
developed.
The principles of UX
data design
visualisation have
been critically and
contextually
developed. They are
well balanced in
terms of theory and
personal reflection
and reflect a
message that is
readily usable.
Research:
methodological
curiosity and
overall user’s
experience
(25%)
There is little
evidence of
research into
relevant
visualisation
methods; poor-
quality
visualisations;
poor overall user’s
experience.
There is internal
evidence of
research into
relevant
visualisation
methods; simple
visualisations;
simple overall user’s
experience.
There is sound
evidence of
research into
relevant
visualisation
methods. The
visualisations are of
good quality and
employ novel
approaches;
appropriate overall
user’s experience.
Strong evidence of
research into
relevant
visualisation method
is evident; good
quality visualisations
that employ novel
approaches and /or
interactive
techniques; good
overall user’s
experience.
Excellent research
into relevant
visualisation
methods is present;
high-quality
visualisations that
employ both novel
approaches and
interactive
techniques;
excellent overall
user’s experience.
COMM2501/5501 Assessment 3, T3 4
Turnitin Submission
Your assignment must be uploaded as a unique document and all parts must be in portrait format. As long as
the due date of the assessment is still future, you can resubmit your work. Note that the previous version of
your assignment will be replaced by the new version.
Assignments must be submitted via the Turnitin submission box that is available on the course Moodle website.
Turnitin reports on any similarities between your cohort’s assignments, and also with regard to other sources
(such as the internet or all assignments submitted all around the world via Turnitin). Please read this webpage
(https://student.unsw.edu.au/turnitin), as we will assume that you are familiar with its content. You can also find
on the Moodle webpage the Turnitin Similarity Report Interpretation Guide (2019).
You need to check your document once it is submitted (check it on-screen). We will not mark assessments that
cannot be read on screen. Students are reminded of the risk that technical issues may delay or even prevent
their submission (such as internet connection and/or computer breakdowns). Students should allow enough
time (at least 24 hours is recommended) between their submission and the due time. The Turnitin module will
not let you submit a late report. No paper copy will be either accepted or graded.
Late submission
Please note that it is School policy that late submission of assignments will incur in a penalty. A penalty of 25%
of the mark the student would otherwise have obtained, for each full (or part) day of lateness (e.g., 0 day 1
minute = 25% penalty, 2 days 21 hours = 75% penalty). Students who are late must submit their assignment to
the LIC via e-mail. The LIC will then upload documents to the relevant submission boxes. The date and time of
reception of the e-mail determines the submission time for the purposes of calculating the penalty.
More information on Late submissions, extensions and special consideration is available in the Moodle course
webpage section Getting started.
Plagiarism awareness
Students are reminded that the work they submit must be their own. While we have no problem with students
working together on the assignment problems, the material students submit for assessment must be their own.
Students should make sure they understand what plagiarism is—cases of plagiarism have a very high
probability of being discovered. More information on Academic integrity and plagiarism is available in the
Moodle course webpage section Getting started.