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.
