COMM2501 Assessment 3, T3 1
Assessment 3: Visualisation Portfolio Blog
Assessment instructions

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 R tutorials 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 your R tutorials. 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. Theme
selection is as follows:
• You may use the provided theme and data sets suggested on the A3: Datasets examples page on Moodle
course page in Assessments section.
• Or, you may choose a theme and data set of your own devising, provided that both are approved by your
LIC by the end of week 3 (by email at [email protected]).
During modules 1-6, you should apply analytical and visual techniques learned from R tutorials 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 a blogdown with R in the labs material).
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. This is an individual work.
See also the assessment rubric below.
Supporting resources
The successful completion of this assessment task is supported by the required weekly training tutorials in R
and Tableau. This assessment evaluates your understanding of these platforms and your competency with
them. The required tutorials provide comprehensive training necessary to complete the task.
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.
COMM2501 Assessment 3, T3 2
Supporting activities:
1. R tutorials, Supporting activity
2. Tableau tutorials, 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 on the Moodle course webpage > Assessments section. See
below more information on the Turnitin submission in page 3.
More details about the submission requirements will be announced at a later date – please regularly check the
course announcements on the Ed forum.
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
Fail Pass Credit Distinction High Distinction
Research and
data analysis:
data description,
data interrogation
and
methodological
cruciality (40%)
There is little
evidence of
research into
relevant
visualisation
methods;
poor
application of
analysis
methods;
misinterpreted
source data
and/or poor-
quality
visualisations.
There is
internal
evidence of
research into
relevant
visualisation
methods;
limited
application of
data analysis
methods and
simple
visualisations.
There is sound
evidence of
research into
relevant
visualisation
methods, with
appropriate
application of data
analysis methods.
The visualisations
are of good quality
and employ novel
approaches.
Strong evidence of
research into
relevant
visualisation
method is evident.
There is appropriate
and extended
application of data
analysis methods
and good quality
visualisations that
employ novel
approaches and /or
interactive
techniques.
Excellent research
into relevant
visualisation
methods is present.
There is
appropriate and
extensive
application of data
analysis methods
and high-quality
visualisations that
employ both novel
approaches and
interactive
techniques.
Data storytelling;
ability to weave a
narrative based
on data (40%)
There is little
or no
relationship
between data
visualisations,
insights and
the data story.
There is some
relationship
between data
visualisations,
insights, and
the data story.
There is a solid
relationship
between data
visualisations,
insights, and the
data story. The
visualisations
demonstrate
progression of
ideas.
There is strong
relationship
between data
visualisation,
insights, and the
data story. The
visualisations
demonstrate a
progression of ideas
and methodologies
There is an
inspired and novel
relationship
between data
visualisation,
insights and the
data story. The
visualisations
demonstrate
progression of
COMM2501 Assessment 3, T3 3
that assist
knowledge
formation.
ideas and
methodologies that
assist knowledge
formation.
Design of data
visualisation:
effectiveness,
simplicity and
usability of
visualisation to
convey message
(20%)
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 justification
provided. The
message is
conveyed simply
and useably.
The principles of UX
data design
visualisation are
logically and
effectively justified.
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.

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 Module 0.
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.
COMM2501 Assessment 3, T3 4
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 Module 0.

欢迎咨询51作业君