COMM1190 Assessment 2: Team Report
Individual Component: Due Week 7 3:30 pm Monday (AEDT) (10 marks)
Team Component: Due Week 8 3:30 pm Monday (AEDT) (20 marks)
30%
A written report
Individual Component: 2-page limit excluding references and appendix.
Team Component: 3-page limit excluding references and appendix.
Via Turnitin on Moodle course site
Brief
Australia’s Globe Trotters, a travel booking company that re-sells international and domestic flights, recently conducted a survey to gauge consumer satisfaction with their website. Intrigued by the internal memo, the Customer Insights team manager has asked you and several other analysts to work together to understand what is driving customer website satisfaction and what actions the Customer Insights Unit should take.
Since the data was collected in 5 waves, the manager has asked that each team member prepare their own individual 2-page technical report on one segment of the data to determine what they think drives customer website satisfaction using a model of the survey and website data.
Next, the Manager wants the team to choose one model to explain website satisfaction and make appropriate conclusions and recommendations for the next steps. The recommendation should be presented in a one-page executive summary, providing context to the scenario and analysis. The Customer Insights team manager will provide this short report to senior management. To ensure credibility and reproducibility, the manager requires the entire analysis to be conducted in R and has asked your team to prepare a two-page report summarising the rationale and strategies for selecting the chosen model. This technical report will be internal to the Customer Insights team.
To provide richer insights, the manager has asked IT support to provide additional data to each team member. IT support provided data on whether each unique customer who completed the survey has since returned to the website. They also linked customers with Google reviews and provided a column indicating if the review had a positive or negative sentiment and whether the Google review mentioned anything about the website. The manager is unsure about the extra data points’ helpfulness.
Task
Using R, you will explore a dataset and use predictive modelling to address the manager’s queries. You will have to undertake the following individual and team tasks. Individual Report:
• Present a predictive model explaining website satisfaction.
• Describe other candidate models you considered but did not select as your preferred model.
• Explain your rationale and strategy for selecting your preferred model.
• Clearly, concisely and professionally communicate your results and analysis for a more technical audience.
• There is a two-page limit (You do not need to provide general context to the problem).
• Submit your R-code as an Appendix to this document.
• Each team member must work with a different segment of the data.
Team Report:
• Present a predictive model explaining website satisfaction.
• Explain the synthesis processes of 1) model selection, which should be rooted in the models submitted by individual team members, and 2) how your analysis support the findings, conclusions, and recommendations.
• Clearly, concisely and professionally communicate the context, findings, and recommendations for a managerial non-technical audience in a 1-page executive summary.
• Clearly, concisely and professionally communicate the chosen model, model selection process, and any relevant results and analysis for a more technical audience in a 2-page report.
• Submit all relevant team-based R-codes as an Appendix to this document.
Guidance on Data Analysis:
• Critically and collaboratively, reflect on each team member's feedback from assessment 1 and use it to develop your team project where applicable.
• Use descriptive analytics to identify the key factors impacting a user’s app satisfaction. Descriptive Analytics refers to statistics and visualisation techniques. For example, a box plot and a bar chart are different techniques.
• Use predictive analytics to diagnose and/or forecast factors that influence users’ app satisfaction. Predictive Analytics refers to linear regression and regression trees modelling 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).
• For each modelling technique (e.g., linear regression, decision tree, etc.) you use, consider trying 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 its goodness of fit and performance in predicting the outcome variable.
• Importantly, ensure that your predictors make sense and provide evidence that your conclusions are insensitive to the precise model presented.
• If your core conclusions change with variations in appropriate models, then try to explore the data more to understand the differences.
• Only use methods and criteria learned from this course to evaluate performance.• Develop coherent logic from your business issue identification to your variables and modelling techniques selection and your recommendations.
• Explicitly state any key assumptions that impact your data analysis.
• Note: the dataset and the data dictionary will be provided separately.
Requirements and Mark Breakdown:
|
Criteria |
Description |
Ind. |
Grp. |
Total |
|
Problem Analysis |
Apply rigorous analysis, appropriate frameworks, tools, and standards to develop and/or evaluate data and models. |
8 |
10 |
18
(60%)
|
|
Quality of Conclusions and Recommendations |
Develop well-reasoned, appropriate conclusions and recommendations supported by the data and analysis. |
0 |
6 |
6
(20%)
|
|
Communication and organisation |
The report is professionally presented, using language to convey ideas and information effectively and accurately. |
2 |
4 |
6
(20%)
|
Submission Instructions
Individual Component (10 Marks)
This document, to be completed individually, is due Monday, Week 7. We suggest you finish this task earlier to provide more time for the collaborative component.
If you do not submit a credible attempt for the individual component by the Monday Week 7 deadline, you will not receive marks for the team component.
Team Component (20 Marks)
This document, to be completed as a team, is due Monday, Week 8. We suggest you do not approach the task using divide and conquer approaches. Instead, work collaboratively on each section of the reports.
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 must 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 analyses in the team report.
5% of the marks available for the assessment will be deducted for this assessment if you do not submit a fully completed and signed cover page.
Page Limit
Your report will be evaluated on its quality, and one dimension of the quality is expressing your ideas and analysis concisely. There is a three-page limit. Additional pages will result in a penalty. Specifically, 5% of the marks available for the assessment will be deducted for every 100 words over the three-page word limit.
Late Submission Penalties
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 for serious illness, misadventure, or bereavement, which must be supported with documentary evidence. Requests for extensions must be made through special considerations. Note: A request for an extension does not guarantee that you will be granted one.
Studiosity English Support
Studiosity is available on the COMM1190 Moodle Site. Using the service, you can:
• Submit your drafts to a Studiosity tutor for comprehensive feedback on your writing, typically within 24 hours; or
• Connect to a Studiosity tutor in a live one-on-one session about writing.
• Receive comments on various writing areas, including clarity of your ideas, grammar, organisation etc.
• Use up to 2 hours on Studiosity reviews
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
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 meeting minutes/notes and emails.
Note: non-university platforms such as Meta/Facebook messages, texts, Whatsapp, etc. will not be considered. If group members are found to be making an inadequate effort or delivering poor quality, 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 relatively lower input into the assignment.
Unequal Contribution
After submitting the assessment. There will be a 72-hour period where any team member can submit a form to report unequal contributions, which can lead to adjusting of marks.
Unequal contributions must be supported by evidence documenting team members’ efforts and contributions using meeting notes and emails. Again, non-university platforms such as Facebook messages, texts, and WhatsApp messages will not be considered. If there are reports of inconsistent efforts in the teamwork component, the quality of this individual submission may also be considered as evidence.
The team’s goal is to be collaborative and prevent unequal contribution, So, if any issues emerge, they must be flagged with the teaching team as early as possible to allow for support in achieving collaborative outcomes. If you are concerned about unequal contribution, your group should develop and record a project management plan, specifying key milestones and each team member’s responsibilities.
Groups Rules
You must form a group of 3 or 4 people with peers from your tutorial section. There is hard maximum of 4 people per group. Once you have your group, please inform your tutor.
Marking Rubric for Team Assessment
|
Criteria |
High Distinction
(85%-100%)
|
Distinction
(75%-84%)
|
Credit
(65%-74%)
|
Pass
(50%-64%)
|
No Bueno
(0%-49%)
|
|
Individual and Team Problem Analysis (60%)
|
Demonstrates a thorough understanding of the business problem or issue, identifies relevant questions and uses appropriate frameworks, tools, and standards to develop and evaluate data.
Explicitly presents a coherent and clear logic between business issues, analytical techniques, and variable selection.
The justification is sound and convincing. Skillfully explores data with compelling explanations of the issues identified.
|
Demonstrates a good understanding of the business problem or issue, identifies relevant questions, and applies appropriate frameworks, tools, and standards to develop and evaluate data. Explicitly presents a coherent and clear logic between business issues, analytical techniques, and variable selection.
The justification is convincing. Explores data with explanations of the issues identified.
|
Demonstrates a satisfactory understanding of the business problem or issue, identifies some relevant questions, and uses some appropriate frameworks, tools, and standards to develop and evaluate data. Presents a somewhat coherent and clear logic between business issues, analytical techniques, and variable selection.
The justification is partially convincing. Explores data adequately with adequate explanations of the issues identified.
|
Demonstrates a limited understanding of the business problem or issue, does not identify relevant questions, and does not use appropriate frameworks, tools, and standards to develop and evaluate data. Does not present a coherent and clear logic between business issues, analytical techniques, and variable selection.
The justification is not convincing. Explores data inadequately with insufficient explanations of the issues identified.
|
Does not demonstrate a basic understanding of the business problem or issue, does not identify relevant questions, and does not use appropriate frameworks, tools, and standards to develop and evaluate data. Does not present any coherent or
clear logic between business issues, analytical techniques, and variable selection.
The justification is absent. Does not explore data adequately with insufficient explanations of the issues identified.
|
|
Individual and Team |
Communication and Organisation (20%)
Uses language effectively and accurately to convey ideas and information, with clear and concise writing that is well-structured and free of errors.
The tables and graphs are presented professionally.
The team report reads cohesively, with consistent writing style and logical flows.
|
Uses language effectively and accurately to convey ideas and information, with clear writing that is well structured and mostly free of errors.
The tables and graphs are mostly presentedprofessionally.
The team report mostly reads cohesively, with consistent writing style and logical flows.
|
Uses language somewhat effectively and accurately to convey ideas and information, but the writing may lack clarity, structure, or contain errors.
The tables and graphs are somewhat professional.
The team report reads somewhat cohesively in terms of writing style and logical flows.
|
Uses language ineffectively or inaccurately to convey ideas and information, with unclear or poorly-structured writing that contains errors.
The tables and graphs are largely unprofessional.
The team report is largely incohesive in terms of writing style and logical flows.
|
Uses language incoherently or inaccurately to convey ideas and information, with writing that is unclear, poorlystructured, and contains numerous errors.
The tables and graphs are all unprofessional.
The team report is entirely incohesive in terms of writing style and logical flows.
|
|
Quality of Conclusions and Recommendations (20%) |
Develops well-reasoned, appropriate conclusions or solutions based on the results of the analysis. The results of each analytic technique performance and findings are correctly interpreted and critically examined supported byacademic references whenappropriate.
Results interpretation is relevant and meaningful inthe case context.
|
Develops good conclusionsor solutions based on the results of the analysis. The results of each analytic technique performance andfindings are correctlyinterpreted and examined.
Results interpretation is generally relevant and meaningful in the case context.
|
Develops satisfactory conclusions or solutions based on the results of theanalysis. The results of each analytic technique performance and findings are partially interpreted and examined.
Results interpretation issomewhat relevant and meaningful in the case context.
|
Develops limited conclusions or solutions based on the results of the analysis. The results of each analytic technique performance and findings are not interpreted and examined.
Results interpretation is not relevant or meaningful in the case context.
|
Develops no conclusions or solutions based on theresults of the analysis. The results of each analytic technique performance and findings are not interpreted and examined.
Results interpretation is notrelevant or meaningful in the case context.
|