EqualView - Performance Review Website

User Interface Prototype
Two groupmates (Bryan and Lara) and I participated in the 3-day Women in Tech Hackathon 2023, where the prompt was to create a digital technology that helps mitigate gender bias in the workplace.

Design Process

  • Ideation - Researching common issues that women experience in the workplace and choose a specific one to address. The issue of bias within performance reviews was chosen. Next, the current solutions implemented were reflected upon, identifying aspects that can be addressed through our website.
  • Personas and User Scenarios - Research studies and interviews that convey how bias in performance reviews have affected women on the day-to-day scale and in a larger scale to isolate the roots that lead to biased performance reviews and create personas and user scenarios that well represent said problems. The personas and user scenarios created ensure that the prototype will stay focused in addressing gender bias.
  • Wireframing - A rough drawing of the website's potential structure was drawn on paper to test which features are necessary and where they should be placed to be most effective.
  • High Fidelity Prototyping on Figma - Creating the first iteration the EqualView website based on the wireframe.
  • Cognitive Walkthrough - Conducting a cognitive walkthrough, we took turns to do tasks such as 'reference Pam's attendance to review her Punctuality' to ensure that they can be done effectively and are intuitive.
  • Finalizing - Make changes to the prototype based on feedback to create the final iteration of EqualView.



Sample User Scenario

Michael, 42, is the manager of Dunder Mifflin, a paper company, and he needs to conduct his quarterly performance reviews. He often finds it difficult to separate his personal feelings when conducting reviews and ends up providing feedback that is not constructive, relevant, or measurable. The women in his office feel that their feedback are often personality-based, such as being too moody or sensitive, whereas the men in his office are reviewed based on how well they work. Michael finds it a hassle to have to access different files and documents to see the sales, attendance, and track record of his employees, so he rarely uses quantitative data as basis for his reviews.

To address his employees' complaints, corporate asks Michael to use EqualView, a website to facilitate performance reviews that can provide him the guidance, accountability, and easy data access for better reviews.


User Flow

Assignment - Users log in and are assigned a list of employees, accessible on the home page and color-coded based on progress.
Starting - Users choose an employee to begin a review draft. Within the review page, on the left is the employee's statistics (attendance, sales, clients, etc.). On the right is where each review topic will receive a rating and written feedback.
SMART - When providing feedback, users are required to provide feedback that is specific, measurable, achievable, relevant, and time-bound. The user's feedback is analyzed by AI and color-coded to identify which parts adhere to SMART. When a feedback is not SMART, a red buoy icon will appear on the bottom left, where users are given reflection questions to better adhere to SMART.
Referencing Employee Statistics - To incentivize the use of objective data as the basis of feedback, users are able to refer to statistics by using the '[' character followed by the section of data wanting to be referenced. By referencing the data, that section of the feedback will be a hyperlink to the data itself, helping those receiving the feedback to quickly access which data is being referred to.
Submitting - Reviews can only be submitted and marked completed when all the feedback is SMART. Considering that AI determines whether the text is SMART, users are able to send an enquiry if they feel that the AI makes a mistake. After, the review will be manually assessed instead.

Website / Desktop Application Walkthrough

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