COMM1190 Assessment 2: Team Report
Final submission: Week 8 Friday, 3rd November 2023 at 5:00 pm (AEDT).
This assignment is graded upon 30 marks (i.e., 30% of the course total
marks)
The assignment should be submitted in the form of a written report and to be
undertaken in groups of 5 students. The team members must belong to the
same tutorial class.
Maximum five pages (~2500 words), excluding tables, figures, references,
and Appendix (please refer to the detailed requirements provided in the
section “Word Limit”)
Submission via Turnitin on the Moodle course page.
Objective
In this team assessment you are required to demonstrate your critical thinking and ability
to conduct predictive analytics using R software and generate insights in the form of
actionable recommendations to solve a business problem.
You are required to work with your team members (your COMM1190 group). Your team
is approached by SwiftFood to conduct a predictive analysis using R to predict the speed
of food deliveries based on key variables identified from the dataset.
In this assessment, you will continue to use the same dataset as in Assessment 1 (the
team leader’s personalized data). Based on the discussion of key findings of your
predictive analysis, you are required to derive actionable insights on how to improve the
speed of food delivery.
The learning content needed to complete this assignment is covered in the course up until
the end of Week 7.
Requirements & assessment criteria
1. Business problem identification (10%)
Accurately identify the problem, formulate insightful research questions, articulate
the purpose of the analysis and the method undertaken. Think of why your analysis
is important and how you plan to undertake it.
2. Data selection and preparation (10%)
Refer to the findings of the descriptive analysis in Assessment 1 to discuss the
relevant variables you would consider in your predictive analysis and any data
transformations you might need to perform. For each variable available in the
dataset, think of whether it should be transformed to better serve the needs of the
predictive analysis. e.g. turning a continuous variable into a categorical variable,
time variable into specific time slots, the variable Order_Time_Placed could be
transformed into Order_Hour (e.g. 23:25:00 into 23), etc.
3. Predictive Data Analysis (30%)
Conduct a predictive analysis to forecast how relevant variables impact the speed
of food delivery. Generate at least 2 models and explain why these models are
suitable for your analysis. The 2 models can be generated using either two different
modelling techniques (e.g. linear regression, logistic regression, classification tree,
etc.) or the same technique by trying out different independent variables to predict
the outcome variable.
Compare the generated models and decide what would be the best performing
model to select along with justifications.
4. Business Recommendations (20%)
Interpret the findings of your analysis to identify the key factors influencing the
outcome variable (speed of delivery). Explain how these key factors influence the
speed of food delivery.
Generate insights in the form of actionable recommendations on how to improve
the speed of food delivery. Provide justifications of your arguments based on
examples and/or academic references.
Please note that the insights should be derived entirely from the data and the model
selected.
5. Ethical Considerations (20%)
SwiftFood decides to use the outcomes of your predictive analysis to predict the
delivery duration of each new order received from customers. However, after
application of your model, they noticed that some orders were not delivered within
the predicted time frame. To address this issue, SwiftFood asked your team to
consider collecting the real-time GPS locations of each delivery person.
Discuss the ethical implications you would consider during the data collection,
analysis and communication (10%). Apply relevant ethical theories to justify and
support your arguments. Provide suggestions on how to prevent and/or mitigate
these ethical issues (10%).
Supplementary reading: Consult Danish Design Centre’s Digital Ethics
Compass (https://ddc.dk/tools/toolkit-the-digital-ethics-compass/) to
understand the nuances of data ethics in the context of digital products
6. Communication and Organization of your written 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.
• Use 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.
NB: A detailed rubric is included on the last pages of this document.
Guidance on Data Analysis
1. Critically and collaboratively reflect on each team member’s feedback from their
individual assignment and use them to develop your team project where applicable.
2. Refer to the results of the descriptive analytics in assessment 1 to identify the key
variables that may impact the speed of food delivery. Descriptive Analytics refer to
statistics and visualization techniques. For example, a box plot and a bar chart are
different techniques.
3. Conduct predictive analytics to estimate the key factors that influence speed of
food delivery. Predictive Analytics covered in this course include linear regression,
logistic regression, and decision tree modelling techniques. It is required that you
use the modelling techniques discussed in lectures and workshops (i.e., do not use
modelling techniques beyond the scope of this course).
4. Consider trying out several models / modelling techniques (at least 2) and explain
which model can be considered the “best”. To select a model to be the “best” out
of your candidate models, you can assess it based on the model’s goodness of fit
and/or 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). You may
also compare several models using the same modelling technique by trying out
different independent variables to predict the outcome variable.
5. Depending on the modelling technique chosen, you may need to perform data
transformation. e.g. some modelling techniques require transforming a numerical
dependent variable into a binary or categorical variable.
6. Develop coherent logic from your business issue identification to your variables
and modelling techniques selection, your data transformations, insights derived
from the findings of your analysis in the form of actionable recommendations.
7. Explicitly state any key assumptions that impact your data analysis.
Project Management
During your work on this project, please make sure that you follow the USNW Guide to
Group Work (must read) to participate in the team project.
Develop and record a project management plan by specifying key milestones and each
team member’s responsibilities.
Nominate a team project leader to facilitate collaboration and submit the report on behalf
of the group.
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.
Submission Instructions
The team leader or a designated team member needs to submit the written report via
the Turnitin submission link on Moodle. Please Note that only ONE report from a group is
required to be submitted.
You are required to submit your report in a Word format, accompanied by a cover sheet
(provided on Moodle). Please note that you need to nominate a team lead on the cover
sheet by specifying their name and zID.
Please include all relevant R codes in the Appendix. The codes should take the raw data
file provided as the input and must be able to reproduce all the data analysis reported.
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 only be accepted after
five days (120 hours) of the original deadline if 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. Please note that no extensions will be granted except for serious illness,
misadventure, or bereavement, which must be supported with documentary
evidence. Requests for extensions must be made to the Course Convenor by email
and be accompanied by the appropriate documentation 24 hours before the due
date of the assignment. Students must apply for Special Consideration if this is not
possible.
3. Applications for Special Consideration must be submitted via myUNSW to be valid.
Information on when and how to apply for Special Consideration can be found here:
https://www.student.unsw.edu.au/special-consideration. If your email asks to
confirm receipt of an application, please be aware that we will only reply if we have
not received your application.
4. The Course Convenor is the only person who can approve a request for an
extension. If you request an extension, the Course Convenor will email you the
decision. Note: A request for an extension does not guarantee that you will be
granted one.
Word Limit
Your report will be evaluated based on the depth and the quality of the analysis (you are
required to perform a rigorous analysis) and the explanations of the findings & insights.
Hence, we suggest a maximum number of five pages. A penalty will not be applied if your
report stays below five and a half pages.
Over and above the leeway, a penalty of 5% of the available marks for the assessment
will be deducted for each extra half page.
Please note that 1% of the available marks for the assessment will be deducted for this
assessment if you do not include a completed and signed cover page as part of the report.
Studiosity English Support
UNSW has partnered with Studiosity to provide online writing support. It is an online
platform, freely available 24/7 for our students, providing focused feedback on structure,
grammar, referencing, and choice of language but not on the course content. A link to the
Studiosity is accessible on Moodle. Please note that you have up to 6 submissions per
term. You can also find additional academic writing resources at
https://www.student.unsw.edu.au/referencing.
AI Tools and Academic Integrity
The use of AI or ChatGPT to help you learn the R codes is encouraged. However, the use
of ChatGPT for writing the report will be subject to an AI-detective tool, which may lead to
academic integrity investigations. In general, you must comply with academic integrity and
avoid all forms of plagiarism. This includes buying essay/writing services from third parties,
and engaging another person to complete your assessment, whether the latter is paid or
not.
UNSW Guide to Group Work
“This page will inform you about the nature of group work, what you should expect, and
the expectations teachers have of you in group learning situations.” Access via
https://www.student.unsw.edu.au/groupwork. Furthermore, please refer to the Modus
Operandi next page.
Modus Operandi
1. Group Formation
• Students are expected to reach out to the other class participants for team
formation,
• Students are encouraged to choose the teammates who are aspiring to match their
commitment in time and effort. The groups can be communicated to their tutors as
soon as possible.
• In every team, the maximum number of students is strictly limited to five. The tutor
has the ultimate discretion to reshuffle a group if there are unexpected drop-outs
or for other reasons. If your groups fall short of the size of 4-5, you can be granted
an extended deadline from Monday 6 November.
2. Meetings
• At the inception of the team formation by the end of week 4, shall initiate a first
exchange of contacts by email and alternative means of communication (e.g.,
WhatsApp or mobile phone number). If any team member fails to respond to their
teammates within 24 hours, the remaining members can contact their respective
tutors.
• All teams are strongly recommended to conduct at least two meetings with an
adequately set agenda at a mutually agreed time, the first at the beginning to
discuss the task allocation and the expected milestones for deliverables, and the
second at the end to fine-tune their work and bring them together.
3. Team Contract
• The use of the Gantt Chart is instrumental in task scheduling.
• The team members should sign a team charter, outlining the details of task
allocation, the mode and frequency of communication, and the rules of
collaboration. The team charter shall be added as an appendix to the finalized
assignment report as evidence of effective team project management.
• The team must appoint a leader, whose zID data set file will be used for the
assessment 2. The role of the team leader is to coordinate that all members are
participating and communicating with each other regularly.
• The mark awarded will be assigned to all team members, but individual marks may
be moderated if the peer assessment and subsequent investigation identified an
uneven contribution and effort across the group members.
4. Communication
• Every member of the team must attend a weekly 30-mins meeting with their team,
to discuss and clarify expectations, as well as checking-in the progress of their
respective deliverable.
• A shared Drive can give access to all team members who can review and edit their
common report, thus fostering real-time collaboration and smooth tracking of
members’ work in one place.
• Team bonding is a crucial element of effective collaboration, whereby the
teammates agree to exchange communication details (WhatsApp or phone
numbers). They can opt for any preferred forms of social media or traditional
means of communication.
5. Dispute and Resolution
• Your tutor will intervene if there are unresolved contentious issues as a matter of
last resort, whereby all communication channels have failed to achieve harmony
and civility among team members. Yet, teams should involve respective tutors as
early as possible whenever there are unequal contribution issues or disputes.
6. Peer Review
• At the end of each deliverable, each team member is expected to review and
provide feedback on each other's work to enhance the quality of the overall report.
The peer assessment review can be reported to the Course convenor in the event
of obvious unequal contribution-namely non-participation by a team member or a
member who is not meeting the deadline. Then, the team member can fill in a
written statement of unequal contributions and email comm1190@unsw.edu.au on
or before the assessment 2 report’s due date.
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 miss 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
issue(s) and address its
importance using
industry examples.
Data Analysis
(40%)
- Data selection and
preparation (10%)
- Predictive Data
Analysis (30%)
▪ 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.
▪ No split of training
and test data
▪ Identify one predictive
analytical technique 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 reason is unclear.
▪ The results of the
analytics model are
somewhat correctly
examined and
interpreted.
▪ R codes are included,
and extensive errors
are identified.
▪ The trained model was
selected but not
evaluated
▪ Identify and explain
the predictive
analytical techniques
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
examined and
interpreted.
▪ R codes are included,
and some errors are
identified.
▪ Identify, explain, and
justify the predictive
analytical techniques
for solving the 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.
▪ The results of the
analytics model
performance and
findings are mostly
correctly examined and
interpreted, supported
by academic
references.
▪ Identify, explain, and
justify the predictive
analytical techniques for
solving the business
issue.
▪ Use descriptive analytics
to identify, explain, and
justify variables for
deploying each
technique. Justifications
are sound and
convincing.
▪ Explicitly present a
coherent and clear logic
between business
issues, analytical
techniques, and variable
selection. The reason is
coherent and clear.
▪ The results of the
analytics model
performance and findings
are correctly interpreted,
critically examined, and
supported by academic
references.
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
▪ The trained model
has been evaluated
but not properly
interpreted
▪ R codes are included,
and errors are identified
occasionally.
▪ The trained model has
been evaluated and
interpreted.

▪ The model is
parsimonious and has
been properly evaluated
and interpreted.
▪ R codes are included
without errors.
Business
Recommendations
(20%)
▪ Inadequate or no
recommendations
are provided.
▪ Develop
recommendations, but
they may contain many
weaknesses or
limitations.
▪ Recommendations are
inconsistently tied to
some of the issues
discussed.
▪ Develop
recommendations,
but they 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, accompanied
by critical thinking.
Ethical
Considerations
(20%)
▪ No ethical issues
are identified.
▪ No suggestions
are provided.
▪ two 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.
▪ two 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.
▪ three ethical issues are
identified.
▪ Each ethical issue is
connected with the
business context using
an example.
▪ Suggestions
adequately address
each issue identified.
▪ three 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.
Communication and
Organization of
Report
(10%)
▪ Your writing is not
professional in
tone and has
major spelling and
grammatical
errors.
▪ Some attempt has
been made to use a
professional tone and
presentation in your
writing, but there are
some spelling and
grammatical errors.
▪ Your writing is mostly
professional in tone
and presentation, but
occasional spelling
and/or grammatical
errors exist.
▪ Your writing is
professional in tone
and presentation, with
a few very minor
spellings and/or
grammatical errors.
▪ Your writing is
professional in tone and
presented outstandingly
with no spelling or
grammatical errors.
▪ Your written expression
provides a strong and
Criteria
& Weight
Fail
(0% - 49%)
Pass
(50% - 64%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(85% - 100%)
▪ Your written
expression does
not indicate a
logic/flow between
each essay
section.
▪ 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.
▪ No executive
summary is
provided.
▪ You have endeavoured
to provide logic/flow
between each essay
section.
▪ 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.
▪ An executive summary
is provided but missing
key aspects of the
report.
▪ Your written
expression indicates
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.
▪ An executive
summary is provided
and covers essential
aspects of the report.
▪ Your written expression
strongly indicates 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.
▪ An executive summary
is provided and covers
essential aspects of the
report using non-jargon
language.
coherent indication of the
logic/flow between each
essay section, enabling
key arguments to
develop fully.
▪ Good structure with
organized headings and
coherent follow between
sections.
▪ All sources are
referenced throughout
the essay, and the seeds
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.