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Assessment (non-exam) Brief

Module code/name	MSIN0006 Business Intelligence
Academic year	2024/25
Term	1
Assessment title	Coursework 1 – Sessions 1-5 Individual Research for Pottermore Project
Individual/group assessment	Individual

Section A: Core information

Submission date	01/11/2024
Submission time	10:00am UK time
Assessment is marked out of:	100
% weighting of this assessment within total module mark	30%
Maximum word count/page length/duration	1500 words
Footnotes, appendices, tables, figures, diagrams, charts included in/excluded from word count/page length?	All of these items are included in the word count IN-TEXT CITATIONS ARE INCLUDED IN THE WORD COUNT
Bibliographies, reference lists included in/excluded from word count/page length?	References/bibliographies are excluded from the word count
Penalty for exceeding word count/page length	Penalty for exceeding word count will be a deduction of 10 percentage points, capped at 40% for Levels 4, 5, 6 and 50% for Level 7).Refer to Academic Manual Section 3: Module Assessment – 3.12 Word Counts.
Penalty for late submission	Standard UCL penalties apply. Students should refer toAcademic Manual Section 3: Module Assessment – 3.11 Deadlines and Late Assessment.
Artificial Intelligence (AI) category	Assistive
Submitting your assessment	The assignment MUST be submitted to the correct submission box on Moodle for this module by the relevant deadline. Submit early because technical problems and delays affecting submissions do not excuse late penalties. You are responsible for ensuring that the file you submit is the correct file and readable on Moodle. Any cases of submissions timestamped after the deadline, including repeat/ corrected submissions, will be considered late.
Anonymity of identity. Normally, all submissions are anonymous unless the nature of the submission is such that anonymity is not appropriate, illustratively as in presentations or where minutes of group meetings are required as part of a group work submission	The nature of this assessment is such that anonymity is required.

Section B: Assessment Brief and Requirements

Read and follow all instructions in this official brief. It supersedes any other representations made verbally by instructors, TAs, or others. No other criteria will necessarily be applied to grading, including representations or file submissions outside of the instructions or after the fact.

This coursework requires individual, not group or collaborative analysis and writing. Upon submission, you are advised to check the similarity score of your submission with the link beneath the submission box to avoid collusion, which is formal academic misconduct.

Details of the assessment brief

Essay Instructions

Write a three-part essay style. report about your individual research in support of the Pottermore project that responds to the three questions below. No title page, introduction, tables, figures, charts, vizzes or summary should be included: just answer the questions in short essay style. It is your obligation to submit a report that would be brief, credible and informative to a Pottermore senior manager.

You must cite all taught course concepts that you use in your essay to the reference provided in the lecture slides or, if not provided, cite the lecture slide. Use “(Author, Year, page/slide if applicable)” format for in-text citations. In general, you should consider the relevance to your report of each concept taught in each week. However, you are required to make critical choices about the most relevant taught concepts to include in your report, and less relevant details that should be omitted to demonstrate educated critical choice. You must also cite and reference all external sources, including data sources, that you report in your essay. Citations must also be included in a References section at the end of the document. Citations and references must use Harvard referencing style. with fuller details, where available, such as the URLs of external data sources.

Do NOT introduce theories/concepts/frameworks outside of those taught in MSIN0006 lectures and seminars. Doing so will be evidence of (1) failure to understand the relevance of taught concepts and (2) essay produced by AI, a serious academic misconduct.

Structure and label each section exactly according to the questions in the following list. Be aware that graders will ONLY look for the answer within the structure specified herein. They will NOT go looking for answers in other sections or poorly structured paragraphs and documents.

Q1. A Proposition

Use the Pottermore (CEO, Mike Nuttal) interview video to write a short essay answer that covers the following points along with your reasoning. State one PROPOSITION you considered that is different from other group members in the following form. Outcomes that represent a problem raised by the CEO; Factors that might logically affect the problem; and Why the factors should effect the Outcomes. How might you plausibly ‘measure’ changes in the Outcome? How might you plausibly ‘measure’ changes in the Factors? Would the required data for these measures come from internal systems (Pottermore data) or external data? Include no tables or figures. A high-grade essay will demonstrate mastery of all Grading Criteria below.

Q2. Data Gathering

Look for external data (if applicable) or Pottermore data on Tableau Online on your own. Write a short essay answer that covers the following points along with your reasoning. Name and describe one data source. Which columns of data, by name (pick one that is different from other group members), could you use to measure the Outcome or a Factor in your group’s Proposition? The proposition and dataset for this answer can be different than your answer to Q1. What does the column measure? WHY does it satisfy or not satisfy our needs? Include no tables or figures. A high-grade essay will demonstrate mastery of all Grading Criteria below.

Q3. Data Preparation

Analyse at least one column of data (external data if applicable or Pottermore data on Tableau) that is relevant to a Factor OR Outcome of the group proposition and also different from other group members. The proposition and dataset for this answer can be different than your answer to Q2. For this answer, it does not matter if the result is not what you expected. Interpreting your plot, write a short essay answer that covers the following points along with your reasoning. What problem can you find in the raw data for your need to analyse this data in Tableau? If possible, you can consider if the data is changing much over time or not; is the data clean, consistent, etc? Do not write about solving a problem; write to explain the problem. Include no tables or figures. A high-grade essay will demonstrate mastery of all Grading Criteria below.

Grading Criteria

To earn a high individual grade on this assessment, each essay answer must demonstrate mastery of all the following criteria:

1. Use Taught Theories & Dimensions (Abduction)

The most relevant CONCEPTS/FRAMEWORKS/THEORIES TAUGHT IN THIS MODULE should be explicitly applied in sufficient detail, and significantly less relevant detail is omitted. See coursework brief and related lecture notes to make an educated choice about the 'REQUIRED' and 'EXPECTED' concepts/ theories/ dimensions for this question. A strong answer clearly and correctly identifies and defines the concepts/ theories/ dimensions it uses, even if the question does not explicitly say so. 'Theory' can refer to any formal concept, framework, model, method, procedure.

2. Use Cause-effect Logic to Explain (Deduction)

Cause-effect logic should be clearly explained for every claim in every paragraph. Like a hypothesis, explanatory logic identifies a specific variable (cause) and an action by which it produces an outcome (effect). Only theory or other formal concepts that are taught in this module will be accepted. The logic statement should be falsifiable (testable) with empirical data. It should use active transitive verbs (e.g., "subject-action-object") rather than passive or intransitive verbs (e.g., "is", "will be", "was"), which are vague. The author should surface assumptions as limitations or conditions under which the argument would hold and conditions that, if changed, could alter the conclusion.

3. Use Evidence for Logic (Deduction)

Qualitative or quantitative 'data' should be presented that evidences each claim by showing whether the necessary and sufficient conditions for the logic for the claim are true. Such data is meaningless as evidence if it is does not explicitly relate to a logic statement. It can also be meaningless if presented as raw data without context, where context should be communicated through comparison, proportion or significance.

4. Give Conclusions & Consequences (Induction)

Interpret with judgement any conclusions, since acting on any conclusion will likely have different consequences for different stakeholders. Multiple perspectives/alternatives should be explicitly compared, trade-offs well analysed, and recommendations should explicitly attempt to balance trade-offs.

5. Follow Referencing Instructions

All required in-text citations and references must be included and accurately formatted to Harvard referencing specification. All referenced documents must be accessible via UCL library or Google. Inaccessible references generated by AI will result in zero score for this criterion and may be investigated for academic misconduct.

See also Section E: How your work is assessed below in this document.

Section C: Module Learning Outcomes covered in this Assessment

This assessment contributes towards the achievement of the following stated module Learning Outcomes as highlighted below:

• Reformulate complex problems to plan a fruitful approach to solving them;

• Manage processes of identifying, gathering, generating, and analysing critical business information;

• Apply techniques, technologies, processes, and applications to internal business data to support effective decision-making;

• Understand how to integrate company data with data from the Internet to derive insights;

• Evaluate, select, and manage appropriate approaches to conducting a business intelligence project;