COMM1190 Assessment 2: Team Report
Week 8: 3:00 pm Friday November 4 (AEDT)
30%
A written report
Maximum word count of 2000, excluding tables, figures, references, and
Appendix (detailed requirements are provided in the “Word Limit” Section
below)
Submission via Turnitin on Moodle course site
Objective
The objective of this team assessment is to test your ability in conceptualizing and
solving analytics problems, your skills in R programming, your knowledge of the ethical
use of data, and your ability in providing business recommendations based on
analytics results. In this assignment, you will form a data analytics team with your
peers. You are expected to analyze data using descriptive and predictive techniques.
The learning content has been covered in the course up until the end of Week 7.
Description
In this team assessment, you are required to take the business scenario in the
individual assessment further to generate actionable insights on how to improve
voluntary superannuation contributions and application satisfaction on RoundUps.
Recall that:
Moneysoft Private Limited is a provider of Financial Technology (FinTech) solutions.
They’re a Sydney-based startup backed by Link Administration. “MoneySoft
RoundUps” is one of their products that support superannuation funds to engage and
retain their members. Superannuation fund providers typically struggle to engage their
users and have trouble communicating the benefits of making voluntary contributions
to investment accounts of superannuation funds. RoundUps tries to solve this
challenge by letting people make small, frequent contributions to their investment
accounts by automatically rounding up the spare change from everyday transactions
(such as buying a cup of coffee). The leadership team is seeking to explore the factors
that are associated with the voluntary superannuation contribution of users on the
RoundUps app. Moneysoft has contracted you as a data analyst to investigate these
factors.
Moneysoft has updated the data set and collected additional data on variables: Salary
Sacrifice, and Application Satisfaction Rating. An updated data dictionary has been
shared with you in a separate file.
Moneysoft requires you to:
1. Form an analytics team to use descriptive and predictive analytics techniques
to generate actionable insights on how to improve user’s voluntary
superannuation contribution and application satisfaction.
2. Reflect on the feedback from your individual project and take it further to help
Moneysoft predict the factors that influence a) user’s superannuation
contribution and b) application satisfaction.
3. Suggest the ethical considerations related to the analysis and the use of data
for enhancing voluntary superannuation contribution and application
satisfaction.
4. Submit your findings in the form of a written report by 3 pm November 4th
(Friday) via Turnitin on Moodle.
How to Download Data
Download the team leader’s personalized data, which is available in the folder:
COMM1190-5229_01327: Assessment2_team_leader_files (unsw.edu.au)
Note that each team will have a personalized data set. Hence, different results and
recommendations may emerge across teams even when using the same analytics
technique.

Guidance on Data Analysis
1. Critically and collaboratively reflect on the feedback that each team member
has received from their individual project and use them to develop your team
project where applicable.
2. Use descriptive analytics to identify the key factors that may impact a user’s
voluntary superannuation contribution and application satisfaction. Descriptive
Analytics refers to statistics and visualization techniques. For example, a box
plot and a bar chart are two different techniques.
3. Use predictive analytics to forecast the factors that influence users’ voluntary
superannuation contribution and application satisfaction in the future. Predictive
Analytics refers to linear regression, logistic regression, and decision tree
modelling techniques. For example, linear regression and logistic regression
are two different modeling techniques. You should use the modelling
techniques discussed in lectures and workshops (i.e., do not use modelling
techniques beyond the scope of this course).
4. For each modelling technique (e.g., linear regression, decision tree, etc) you
use, consider trying out several models using different independent variables
to predict the outcome variable and present the “best” model in your report. To
select a model to be the “best” out of your candidate models, you can assess it
based on the goodness of fit of a model and its performance in predicting the
outcome variable. You should use methods and criteria learned from this
course to test the goodness of fit and its performance (i.e., do not use methods
and criteria beyond the scope of this course).
5. Develop coherent logic from your business issue identification to your variables
and modelling techniques selection, and your recommendations to Moneysoft.
6. Explicitly state any key assumptions that impact your data analysis.
Requirements
1. Business Issue Identification (10%)
• State business issues that your report seeks to address. Examples of
business issues:
➢ What are the key factors associated with voluntary
superannuation contributions? How do these factors influence
voluntary superannuation contributions?
➢ What are the key factors associated with application satisfaction
contribution? How do these factors influence users’ satisfaction
with the application?

2. Data Analysis (40%)
• Use appropriate descriptive analytics techniques and/or a relevant
industry context about voluntary superannuation contribution and
application satisfaction to identify key variables for predictive analysis.
• Use predictive analytics modelling techniques to forecast how certain
variables may impact voluntary superannuation contribution and
application satisfaction.
• Justify the selection of variables and analytics techniques.
• Interpret analytics results.
3. Business Recommendations (20%)
• Provide recommendations based on analytics results.
• Support recommendations based on established industry practices
and/or academic references.
4. Ethical Consideration and Suggestions (10%)
• Identify ethical issues in relation to data collection, data analysis, and
data communication.
• Provide suggestions to avoid and/or mitigate the issues.
• Supplementary reading:
o Consult Danish Design Centre’s Digital Ethics Compass
(https://ddc.dk/tools/toolkit-the-digital-ethics-compass/) to
understand the nuances of data ethics

5. Project Management (10%)
• Follow USNW Guide to Group Work (must read) to participate in the
team project.
• Develop a project management plan and record it by specifying key
milestones and each team member’s responsibilities.
• Nominate a team project lead to facilitate the collaboration.
• Reflect on team project management, for example, the issues impeding
effective collaboration; how you would do differently for improvement if
you had the time again (150 words in maximum).
Note that if any issue emerges from the collaboration and requires the teaching
team’s support, a team should report the issues to the teaching team as early
as possible by involving all team members.
6. Communication and Organization of Report (10%)
• Demonstrate proficiency in writing in English.
• Develop a logical structure to organize the sections of your report.
• Develop an executive summary using jargon-free language.
• Uses figures and/or tables to convey qualitative and quantitative
information effectively and accurately.
• Use academic referencing in Harvard style. Refer to UNSW guideline:
https://www.student.unsw.edu.au/harvard-referencing
• Attach the codes of your R programming (not a screenshot) in the
Appendix of your report.
Submission Instructions
The team lead or a designated team member needs to submit the written report with
all required information via the Turnitin submission link on Moodle. Note that only one
report from a group is required.
Your submission must be in a word or pdf format, accompanied by a cover sheet (to
be provided on Moodle). Please note that you need to nominate a team lead on the
cover sheet by specifying their name and zID.
The appendix must have all relevant R codes. The codes should take the raw data file
provided as the input and must be able to reproduce all analysis that is in the report.
Late Submission
1. Late submission will incur a penalty of 5% per day or part thereof (including
weekends) from the due date and time. An assessment will not be accepted
after 5 days (120 hours) of the original deadline unless special consideration
has been approved. An assignment is considered late if the requested format,
such as hard copy or electronic copy, has not been submitted on time or where
the ‘wrong’ assignment has been submitted.
2. No extensions will be granted except in the case of serious illness,
misadventure, or bereavement, which must be supported with documentary
evidence. Requests for extensions must be made by lodging a special
consideration application.
3. Applications for Special Consideration must be submitted via myUNSW to be
valid. Information on when and how to submit an application for Special
Consideration can be found here: https://www.student.unsw.edu.au/special-
consideration If your email is asking to confirm receipt of an application, please
be aware we will only reply if we have not received your application.
Word Limit
Your report will be evaluated on its quality and one dimension of the quality is being
able to express your ideas and analysis concisely. Hence, we suggest a maximum
word count of 2000. Note that a penalty will not be applied if your report stays below
2200 words (10% leeway applied), excluding tables, figures, references, and
Appendix.
Smarthinking English Support
“… an online writing support platform officially sanctioned by UNSW. Students can
submit drafts of their writing to a Smarthinking tutor or connect to a Smarthinking
tutor in a real-time session and receive comprehensive feedback on a variety of
writing areas”. https://www.student.unsw.edu.au/smarthinking

Smarthinking is available on the COMM1190 Moodle Site.
Using the service, you can:
• Submit your drafts to a Smarthinking tutor for comprehensive feedback on
your writing typically within 24 hours; or
• Connect to a Smarthinking tutor in a live one-on-one session about writing.
• Receive comments on a variety of writing areas including clarity of your
ideas, grammar, organisation etc.
• Use up to 2 hours on Smarthinking reviews.
UNSW Guide to Group Work
“This page will inform you about the nature of group work, about what you should
expect and the expectations teachers have of you in group learning situations.” Access
via https://www.student.unsw.edu.au/groupwork

Groups must plan, schedule and conduct activities in due time. Once groups are
formed, the teams should create and sign up for a teamwork contract that outlines
the terms of engagement. Please refer to the resources presented in the link above.
Groups must meet regularly (at least once per week) while the assignment is being
undertaken and keep a record (diaries, meeting minutes) of such meetings. The
groups must ensure that all members are involved in completing the assignment. The
work is to be divided equally among the group members. All group-related project
management work should be done using a suitable tool such as Trello, Microsoft
Teams or Microsoft Planner.
All group members are expected to work diligently. Group members should contribute
in a valuable and constructive way to the teamwork. Deadlines should be kept, and
work should be delivered at a professional standard. If problems emerge in your group,
then these problems should, in the first instance, openly be discussed in the group
(different members might have different views), and resolutions should be agreed on.
If internal arrangements repeatedly fail to remedy the situation, then you should bring
the issues to the attention of the LIC.
The LIC/ACC may call a group meeting in which each group member will be asked to
describe their input into the assignment and provide supporting documentation of
this effort using individual diary, group diary, meeting notes, emails.
Note: non-university platforms such as Facebook messages, texts, Whatsapp
Messages will not be considered. If group members are found to be making
inadequate effort or delivering poor quality, then they will be counselled to improve
their effort. If sufficient improvement is not made despite group efforts and LIC
interventions, the mark of underperforming group member(s) may be moderated to
reflect the relative lower input into the assignment.



Marking Rubric for Team Assessment
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
Business
Issue
Identification
(10%)
▪ Does not clearly
or correctly
identify or
define/explain an
issue.
▪ Identify and explain
some key elements
of a business
issue(s) but do not
cover all relevant
aspects.
▪ Identify and explain
many key elements
of a business
issue(s) but misses
some relevant
aspects.
▪ Identify and
accurately explain
all relevant, key
aspects of a
business issue(s).
▪ Identify and
accurately explain
all relevant, key
aspects of a
business(s) and
address its
importance using
industry examples.
Data Analysis
(40%)
▪ No relevant
analytical
technique was
identified.
▪ No specific
variables were
identified.
▪ No logic between
business issues,
analytical
techniques, and
variable selection.
▪ The results of the
model are mostly
incorrectly
interpreted.
▪ No R codes are
included.
▪ Identifies at least 1
predictive analytical
technique to be
used for solving the
business issue.
▪ Identify variables
for each technique
to be deployed.
▪ Attempt to present
a logic between
business issues,
analytical
techniques, and
variable selection,
but the logic is not
coherent or clear.
▪ The results of the
analytics model are
somewhat correctly
examined and
interpreted.
▪ Identifies and
explains 2
predictive analytical
technique to be
used for solving the
business issue.
▪ Use descriptive
analytics
techniques to
identify the
variables to be
deployed for
prediction.
▪ Attempt to present
a logic between
business issues,
analytical
techniques, and
variable selection.
▪ The results of the
analytics model are
mostly correctly
▪ Identifies,
explains, and
justifies 2
predictive
analytical
technique to be
used for solvingthe business issue.
▪ Use descriptive
analytics
techniques to
identify, explain,
and justify the
variables to be
deployed for
prediction.
▪ Explicitly present a
logic between
business issues,
analytical
techniques, and
variable selection.
▪ Identifies, explains,
and justifies 2
predictive analytical
techniques to be
used for solving the
business issue.
▪ Use descriptive
analytics to identify,
explain, and justify
variables for each
technique to be
deployed.
Justifications are
sound and
convincing.
▪ Explicitly present a
coherent and clear
logic between
business issues,
analytical
techniques, and
variable selection.
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
▪ R codes are
included and
extensive errors
are identified.
examined and
interpreted.
▪ R codes are
included and some
errors are
identified.
▪ The results of the
analytics model
performance and
findings are mostly
correctly examined
and interpreted
supported by
academic
references.
▪ R codes are
included and errors
are identified
occasionally.

The logic is
coherent and clear.
▪ The results of the
analytics model
performance and
findings are
correctly
interpreted and
critically examined
and supported by
academic
references. The
model is
parsimonious.
▪ R codes are
included without
errors.
Business
Recommenda
tions
(20%)
▪ Inadequate or no
recommendations
are provided.
▪ Develop
recommendations,
but may contain
many weaknesses
or limitations.
▪ Recommendations
are inconsistently
tied to some of the
issues discussed.

▪ Develop
recommendations,
but may contain
some weaknesses.
▪ Recommendations
are consistently
tied to each issue
discussed.

▪ Present insightful
recommendations,
well-supported by
analysis.
▪ Recommendations
are logically and
consistently tied to
each issue
discussed.
▪ Present insightful
recommendations,
well-supported by
analysis, industry
practices and/or
human resource
management
research.
▪ Recommendations
are logically and
consistently tied to
each issue
discussed,
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
accompanied by
critical thinking.
Ethical
Consideratio
ns and
Suggestions
(10%)
▪ No ethical issues
are identified.
▪ No suggestions
are provided.
▪ 2 ethical issues are
identified.
▪ Some issues do
not show direct
connections with
the business
context.
▪ Suggestions are
provided but do not
adequately
address the issues
identified.
▪ 2 ethical issues are
identified.
▪ Each ethical issue
is connected with
the business
context using an
example.
▪ Suggestions are
provided but do not
adequately
address the issues
identified.
▪ 3 ethical issues are
identified.
▪ Each ethical issue
is connected with
the business
context using an
example.
▪ Suggestions
adequately
address each issue
identified.

▪ 3 or more than 3
ethical issues are
identified.
▪ Each ethical issue
is connected with
the business
context using an
example.
▪ Suggestions
adequately
address each issue
identified,
supported by
research or
established
industry practices.
Team Project
Management
(10%)
▪ No description of
how your work is
divided.
▪ No reflection of
your project is
provided.
▪ There is a
discussion on how
the group work
went but the
discussion is
marginal.
▪ Reflections/sugges
ted improvements
are marginal or
generic.
▪ There is a
discussion of how
the group work
worked together
and how the work
could be improved.
▪ These reflections
are of acceptable
quality but could be
more specific (too
generic) or the
reflections are
▪ There is clear
evidence of how
your group worked
together and how
the work could be
improved.
▪ The reflections are
of specific and high
quality.
▪ A graphic
representation
(e.g., table, Gannt
▪ There is clear and
detailed evidence
on how your group
worked together
and how the work
could be improved.
▪ The reflections are
specific, of high
quality, and include
well-justified
improvement
intentions for future
group work.
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
missing important
aspects.
Chart) is supplied
for group work
breakdown.
▪ A graphic
representation
(e.g., table, Gannt
Chart) is supplied
for group work
breakdown.
Communicati
on and
Organization
of Report
(10%)
▪ Your writing is not
professional in
tone and there
are major spelling
and grammatical
errors throughout.
▪ Your written
expression does
not indicate a
logic/flow
between each
section of the
essay.
▪ Poor or unclear
structure.
▪ Your sources
have not been
referenced and/or
there are
excessive errors
in referencing in
the essay.
▪ The word limit
has not been
adhered to.
▪ Some attempt has
been made to use
a professional tone
and presentation in
your writing, but
there are some
spelling and
grammatical errors.
▪ You have
endeavoured to
provide logic/flow
between each
section of the
essay.
▪ Attempt to a good
structure but lack
coherent flow
between sections.
▪ Some sources are
referenced
throughout the
essay, but there
are errors in your
referencing of
sources.
▪ Your writing is
mostly professional
in tone and
presentation, but
there are
occasional spelling
and/or grammatical
errors.
▪ Your written
expression
provides an
adequate indication
of the logic/flow
between each
section of the
essay.
▪ Good structure with
organized
headings.
▪ Most sources are
referenced
throughout the
essay, with only
minor errors in
referencing.
▪ Your writing is
professional in
tone and
presentation with a
few very minor
spellings and/or
grammatical errors.
▪ Your written
expression
provides a strong
indication of the
logic/flow between
each section of the
essay.
▪ Good structure
with organized
headings and
coherent follow
between sections.
▪ All sources are
referenced
throughout the
essay with only
minor errors in
referencing.
▪ Your writing is
professional in tone
and presented in
an outstanding
manner with no
spelling or
grammatical errors.
▪ Your written
expression
provides a strong
and coherent
indication of the
logic/flow between
each section of the
essay that has
enabled key
arguments to fully
develop.
▪ Good structure with
organized
headings and
coherent follow
between sections.
▪ All sources are
referenced
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
▪ No executive
summary is
provided.
▪ An executive
summary is
provided but
missing key
aspects of the
report.
▪ An executive
summary is
provided and
covers essential
aspects of the
report.
▪ An executive
summary is
provided and
covers essential
aspects of the
report using non-
jargon language.
throughout the
essay and the
sources are used
very well, with no
significant errors in
referencing.
▪ A concise
executive summary
is provided and
covers essential
aspects of the
report using jargon-
free language.