Overview

Problem Statement:

Students aspiring for higher education find it hard to make their decision regarding majors, universities, and locations tough. They have very few sources to provide them sufficient information for the above choices.

Solution:

The application guides the students by connecting them to the current students of the universities. It also provides a few universities and majors as suggestions based on a machine learning algorithm, along with an overview of all the universities and their majors. Due to the uncertain location constraint, we chose a mobile application as our solution.

My Role:

This project was a team effort. I led the Empathize and the Prototyping phase of the project.

Steps:

  • Understanding the problem
  • Persona
  • Affinity Mapping
  • Brainstorm
  • Storyboards
  • Low-fidelity sketches
  • Hi-fidelity prototype
  • User testing

Tools used:

  • Pencil and Paper
  • Stormboard.com
  • Balsamiq
  • JustInMind
  • Google Forms

Our Process

1. Understanding the Problem

Talking to current and prospective students we gained a deeper understanding of the problems faced by them and the information they looked for while applying. We conducted:

  • User research
  • User interviews
  • Google survey

2. Persona

Our personas consisted of:

  • Main Persona: Alice Shah – International Prospective Student
  • Secondary Persona: Sahil Nair – International Current Student
  • Tertiary Persona: Tim Smith – American Prospective Student

3. Affinity Mapping

As a result, we had a list of problems:

  • No guidance!
  • No reliable source for information
  • No idea about the university and quality of their majors

Based on the data gathered we conducted an Affinity Mapping:

4. Brainstorm

We brainstormed 30 possible solutions.
We selected 3 ideas:

  1. Website
  2. Mobile application
  3. Virtual reality application

5. Storyboards

We selected Mobile application because:

  • Accessibility in all areas
  • Clearly demonstrate functionalities

Features we focused on:

  • UniHelp: A machine learning algorithm for suggesting personalized list of universities and majors
  • Feature to communicate with current students: one-on-one and by asking questions on a forum called feeds
  • A dedicated page for each university and its courses
  • As an incentive for the current students to participate we chose to award points to helpful answers.

6. Sketches

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7. Low Fidelity prototype

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8. Hi Fidelity prototype

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Testing

We tested the prototype with graduate HCI students to get a better feel of how effective it is.


Goal + Methodology

Goal: To successfully have the user navigate the application with ease, understand the features and complete the tasks.

Methodology: The usability test had:

  1. A set of 3 tasks
  2. A task wise evaluation sheet for the participant to fill out
  3. A survey about the experience

User Testing Tasks

  • Task 1: Sign Up as a prospective student who has decided to go for either HCI or computer science at IUPUI in Indiana
  • Task 2: While browsing, find the university, IUPUI, and the course details of Computer Science in it.
  • Task 3: Open the feed titled, “What are some of the best courses offered at Cornell University?”

Survey link: https://goo.gl/forms/03TkkkG7eimixf0Z2

Learning & Future Scope

  • Participants loved the idea of the product and the flow of the application
  • 2 participants suggested to add a few help pages in the beginning of the app
  • 1 participant brought up the university fee as one of the deciding factors too