2 Assessment Criteria
2.1 Introduction
This document outlines the assessment criteria for assessment one and two of B1703.
2.2 Assessment one
The first assessment for the module is due during Module Week 6 . This assessment is worth 20% of your overall grade for the module.
For this assessment you will submit a 500-word written critique. You will be given a sport data visualisation and will be asked to outline the strengths and weaknesses of this visualisation. You will use published literature to support your critique.
2.2.1 Criteria
Criteria 1: Critique for and against the use of specific data visualisation methods depending on the message the authors want to share and audience they target (85%).
Characteristic 1: Evaluate how well the data is represented in the visualization.
Comment: Your report should consider the appropriate use of figures, colour, clarity, accuracy, and effectiveness in conveying information or telling a story. Consider the audience the author is targeting.
For example you could:
Reflect on the use of colour, does it have a specific function (i.e. reflects team colours), are the different colours purposeful, etc.
Are the chart types the most appropriate for the message which is being conveyed.
Does the author allow for interactivity and how clear is the functionality?
Characteristic 2: Provide constructive suggestions for enhancing the visualization to improve the user experience.
Comment: Your report should showcase a good understanding of the principles of data visualisation and how these can be applied to improve your chosen data visualisation.
For example you could:
Tying in with the reflection in characteristic 1, provide examples of how aesthetics could be used more efficiently.
Include examples of how the author could make the visualisation more engaging and/or user friendly.
Criteria 2: Overall Presentation (15%)
Your report meets the word limit (500 words)
The content is logically organized and flows coherent
Your writing style is clear, professional and adheres to academic standards
You properly reference relevant sources to back up your statement using IEEE format
2.2.2 Grading
Grading will be in line with the University’s guidance on marking (Type B). Each of the five criteria are weighted as indicated above to make up the overall grade for the assessment.
2.3 Assessment Two
The second assessment for the module is due during week 15 (exam period). This assessment is worth 80% of your overall grade for the module.
For this assessment, you will be asked to create a tableau data visualisation based on a publicly available sport data set. You will work through the 7-steps of data visualisation and use R and Tableau to create your dashboard.
You are expected to show evidence of reading in the academic literature, as well as drawing evidence from the practical case-studies included within the teaching programme.
2.3.1 Criteria
The assessment criteria for this assessment are as follows:
Criteria 1: Manipulation, organisation and analysing data in line with your project aim.
(30%)
Characteristic 1: You will demonstrate the ability to acquire and import a dataset relevant to your project aim
Comment: Your visualization should utilize a dataset that aligns directly with your project goals and is relevant to the topic being analyzed. The data source should be publicly available, credible, and reflect the context of your analysis.
For example you could:
- Build a dashboard that analyzes performance trends in marathon runners by sourcing a dataset containing recent race results and timings.
- Select a dataset containing player statistics from the latest sports season if your goal is to identify upcoming talents.
Characteristic 2: Demonstrate effective parsing and cleaning of the data, handling missing values and inconsistencies
Comment: Your visualization and any preparation processes should include thorough data cleaning steps to ensure accuracy and reliability in your analysis. This involves addressing errors, missing values, and inconsistencies while ensuring the dataset is well-structured.
For example you could:
- Remove irrelevant columns or variables that do not add value to your analysis.
- Correct errors such as duplicate entries or invalid data points.
Characteristic 3: Demonstrate the ability to explore the data, identifying patterns, trends, and outliers.
Comment: Your analysis should delve into the dataset to uncover significant insights, including trends, relationships, and anomalies, and these findings should be clearly reflected in your visualizations.
For example you could:
- Apply data transformations like aggregating performance metrics by season or calculating averages to reveal trends over time.
- Use statistical techniques such as correlation analysis to uncover relationships between variables (e.g., training hours vs. performance).
Criteria 2: Use of appropriate data visualisation methods depending on the aim and audience targeted (35%)
Characteristic 1: Design clear and effective visualizations using Tableau (including choosing appropriate chart types, labels, and color schemes)
Comment: Your visualisation should have the ability to highlight or showcase key findings and clearly inform your audience in line with your project aims.
For example you could:
Us a line graph to show trends over time.
Ensure labels are concise and directly explain key metrics, avoiding clutter.
Characteristic 2: Provide meaningful interpretation of the visualizations relating the findings back to the sports data and the goals of the visualization
Comment: Your interpretations should align with the key objectives of the project.
For example you could:
Explain how a particular trend in performance correlates with regulatory changes implemented.
Highlight an underperforming player and suggest potential areas of improvement.
Criteria 3: Create an interactive informative Tableau dashboard sharing sports performance insights (35%)
Characteristic 1: Implement interactive features such as filters, parameters, tooltips, or actions that enhance user engagement
Comment: Use interactivity to allow users to explore the data and gain personalized insights.
For example you could:
Add filters for users to select specific teams, players, or seasons.
Use tooltips to display additional metrics when hovering over data points.
Characteristic 2: Create an organized and user-friendly dashboard with multiple visualizations
Comment: Structure your dashboard for clarity and ease of navigation, ensuring insights are presented logically.
For example you could:
Create separate sections for player performance and team comparisons.
Add headers and descriptions to guide users through the dashboard’s purpose and functionality.
Characteristic 3: Effectively communicate the narrative or story
Comment: Clearly guide the viewer through the insights and analysis.
For example you could:
Use annotations to point out critical moments, such as a significant drop in performance.
Organize visualizations to build a story, starting with an overview and diving into specifics.
Characteristic 4: Show creativity in data representation and visualization.
Comment: Use innovative approaches to make the dashboard visually engaging and insightful.
For example you could:
Design custom icons or logos for teams to add a personalized touch to the visualisations.
Incorporate unique charts/figures going beyond what has been taught in this module.
2.3.2 Grading
Grading will be in line with the University’s guidance on marking (Type B). Each of the five criteria are weighted as indicated above to make up the overall grade for the assessment.