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Data Quest for Honda

RESEARCHER / DESIGNER • WEB / DATA LEARNING

How to Improve the Data Science Learning Experience for Thousands of Honda Employees?

CONTRIBUTION

Researcher
UI/UX Designer 

SKILLS

User Research
Interaction Design
Game Design
Prototyping

TOOLS

Figma

TIMELINE

1/2024 - 5/2024
5 months

OVERVIEW

In collaboration with Honda, I led the team to design of an innovative data science learning game aimed at enhancing employees' data literacy and analytical skills. The game leverages gamification principles to make the learning process engaging, motivating employees to improve their data science competencies in a fun and interactive way.

CONTRIBUTION

Design Research and User Understanding

To ensure the game met the needs of Honda’s employees, I conducted thorough user research. This included:

1. Interviews and Surveys: Engaging with Honda employees to understand their current data literacy levels, learning preferences, and challenges faced in data science training.
2. Persona Development: Creating detailed personas representing the diverse backgrounds and learning needs of the employees, including both data science novices and more experienced users.

Game Design and Prototyping

We designed the game to include various levels and challenges that progressively build data science skills. I focused on designing the game mechanism to make sure our learning objectives are met. Key elements of my contribution include:

1. Interactive Scenarios: Designing realistic business scenarios that require players to apply data science concepts to solve problems.
2. Feedback Mechanisms: Incorporating instant feedback and hints to guide players and enhance learning retention.
3. Rewards and Progress Tracking: Implementing a reward system and progress tracker to motivate continuous learning and improvement.

User Interface and Experience Design

Ensuring a seamless user experience was paramount. My focus areas were:

1. Intuitive Navigation: Crafting a user-friendly interface that is easy to navigate, even for those with minimal gaming experience.
2. Visual Design: Utilizing a clean, modern design with Honda’s brand elements to maintain consistency and professionalism.
3. Accessibility: Ensuring the game is accessible to all employees, including those with disabilities, by following best practices in UI/UX design.

DELIVERABLES

Data Quest / Web

We made this game for practitioner employees in Honda. It is designer for users to analyze datasets, identify trends, and make data-driven decisions in simulated business environments.

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Strategy Quest / Web

We made this game for practitioner employees in Honda. This is designed for desktop use with comprehensive data visualization tools and interactive modules.

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Demo Video

I also directed and edited the demo video to showcase our project for our client Honda, for them to better understand who we're designing for and how the games work in an efficient way.

CONTEXT

Challenges in Data Science Learning

In today's digital era, data science skills are crucial for employees to derive insights and drive competitive advantage. However, traditional training methods often fail to engage and motivate employees effectively. Our goal was to address these challenges by creating a gamified learning experience that would make data science concepts accessible and enjoyable.

PROBLEM 
DEFINITION

Together as the team, under the project scope, we defined the main problems of current employee training program in Honda are as follows. To address these issues, we employed a gamified approach to make learning interactive and engaging, fostering intrinsic motivation and promoting a culture of continuous learning.

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Resource Navigation

Employees often feel overwhelmed by the vast amount of resources available for learning data science.

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Lack of Motivation

There is a general lack of motivation to engage with data science training due to its perceived complexity and lack of relevance to daily tasks.

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Absence of a Learning Culture

Without a supportive learning culture, employees struggle to prioritize and persist in their data science education.

DESIGN
RESEARCH

Understanding Learners

To design an effective learning game, we conducted comprehensive research to understand the target audience:

Literature Review: Analyzing existing research on data literacy training and gamification in education.
User Interviews: Conducting interviews with Honda employees to gather insights on their learning preferences, challenges, and needs.
Persona Development: Creating detailed personas to represent the diverse backgrounds and learning needs of the employees.

Below are the research insights collected.

TA

Target Audience

After segregating and getting data usage, based on the results, we decided to focus on designing for managers and practitioners.

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Gamification Priciples

There are types of gamification principles according to the paper of Selected Gamification Principles (Orji, et al., 2018).

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Data Literacy for Employees

According to Novice data scientist’s empathy map (Doherty, 2020), we established the empathy map of employee about data literacy.

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Instructional Design

We designed the learning cognitive model based on the DIKW model for knowledge management and data value extraction.

Insights

Manager Persona: Managers often possess data but lack the knowledge to utilize it effectively.
Practitioner Persona: Practitioners seek to understand how to leverage data in conversations with business stakeholders.

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DESIGN

Design Workshop

I led a design workshop to brainstorm and develop the game's concept, incorporating insights from our research.

Wireframe and Prototyping

Initial wireframes were created to outline the game’s structure and flow. After the basic site map and wireframe information are confirmed by the clients, I then developed interactive prototypes using Figma to visualize and test key gameplay elements.

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Final UI & Game Elements

After iterating the wireframe prototype, based on user feedback and client requirement, we improved the experience by focusing on the below game elements:

Interactive Scenarios: Realistic business scenarios requiring players to apply data science concepts.
Feedback Mechanisms: Instant feedback and hints to guide players.
Rewards and Progress Tracking: A system to track progress and reward achievements.

Below is the finalized UI prototype.

PRESENTATION TO HONDA

Presentation Highlights

At the end of the project, we presented the demo video and the overview of the game to Honda clients. Below are the highlights we focus on during the presentation:

Introduction: Overview of the project objectives and the importance of data science literacy.

Research Findings: Detailed insights from user research, including personas and learning needs, and how we established our research.
Game Design: Explanation of the game’s design elements, wireframes, and prototypes.
User Testing Feedback: Summary of user testing results and how feedback was incorporated into the final design.
Final Game Demonstration: Live demo showcasing the game’s functionality and key features.

Client Feedback

The presentation was well-received, with positive feedback highlighting the innovative approach and the potential for scaling the game for internal employees.

Blog Post

For more details, please read more in our Medium blog post here.

KEY
TAKEAWAYS

Empowering Employees with Data Science Skills

One of the most significant outcomes of this project was its success in making data science accessible and engaging for Honda employees. By incorporating gamification elements, we transformed a traditionally dry subject into an interactive and motivating experience. This approach not only improved knowledge retention but also fostered a genuine interest in data science, as evidenced by the positive feedback from users who found the game both enjoyable and educational.

Collaboration and Iterative Design Process

The project's success was heavily reliant on continuous collaboration between our team and Honda. Regular feedback sessions and iterative design processes were instrumental in refining the game to meet the specific needs of the users. This collaborative effort ensured that the final product was not only user-friendly but also aligned with the practical requirements of Honda's workforce.

Addressing Real-World Challenges

Through detailed user research, we identified key challenges faced by employees, such as a lack of motivation and the overwhelming amount of data resources. Our game design addressed these issues by providing clear, structured learning paths and incorporating real-world scenarios that employees could relate to. This practical approach helped demystify complex data science concepts and showed employees how these skills could be directly applied to their daily tasks.

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