1. Medium - Fi Rambl Prototype
Clare, Kally, Tevon, Amanda

2. Skip the obvious. Travel with Rambl.

3. Value Prop, Problem and Solution Overview
Value Prop: “Skip the obvious. Travel with Rambl.”
Problem: How can travelers discover less frequented and worthwhile attractions and experiences?
Solution: Gamification of the travel experience on a platform that allows travelers to responsibly share their knowledge of worthwhile attractions, be rewarded by peer travelers who have benefited from that knowledge, and simultaneously, learn about new attractions from their peer travelers.

4. Tasks

5. Task 1 (Simple): Get a curated Itinerary (Plan a Path)
This task remained largely the same. There were a few changes made in the interface.

6. Task 2 (Medium): Rate/Recommend an Activity
This task remained largely the same. However we changed the “stake” term to “stomp” to draw a parallel with the term “footprint” which is a review. A stomp would be the user showing their support for a “footprint” through Rambl points.

7. Task 3: Follow a friend’s trip (Follow a Path)
This task remained largely the same. There were a few changes in the interface design.

8. Revised Interface Design

9. Major Design Changes
For user convenience and ease of use, we implemented a top app bar displaying information and actions relating the current screen. The top app bar contains the following elements.
Title of the current screen.
Collapsed menu icon
Today’s date
The collapsed menu icon contains links to the following:
Home page
User’s account information
FAQ
Rambl History
User’s settings selections

10. Major Design Changes
We decided to implement an FAQ page to clarify terminology usage in our app that may not be clear to new users (i.e. rambl, footprint, stomp, boosting a stomp).
For user’s ease of use and to prevent user error (if the user entered invalid input), we included toggle selection for time increments, for example, on the “Plan a Rambl” page, instead of following our previous plan of having the user input time increments manually.

11. Top App Bar
Toggle selection of time increments
AFTER
BEFORE

13. Medium-Fi Prototype Task Flows

14. Task Flow 1: Get a curated Itinerary (Plan a Path)

15. Task Flow 1 Continued: Get a curated Itinerary (Plan a Path)

16. Task Flow 1 Continued: Get a curated Itinerary (Plan a Path)

17. Task Flow 2: Rate/Recommend an Activity

18. Task Flow 3: Follow a previous Rambl of your own

19. Task Flow 3 Continued: Follow a friend’s Rambl

20. Prototype Overview

21. How did the tool not help?
Prototype Overview
Prototyping Tools:
We used Figma to build an interactive medium-fi prototype, replicating an iPhone 8.1 screen. For our aesthetics, we used elements laid out from Google Material Design, and coolers.co to choose our colors.
How did the tool help?
Figma allowed for easy prototyping
How did the tool not help?
The tool was convenient and easy to use.

22. Limitations/tradeoffs
We hard-coded the following parts of the prototype. In the hi-fi prototype these parts would be implemented using a combination of machine learning and user input. Wizard of oz techniques to make the medium-fi prototype work were also included.
1. Plan a Rambl: the user input of amount of time available is hard-coded to be 3.5 hours.
2. Wizard of Oz: Time passing to generate Rambl and stomps
3. Lunch/Dinner/Movie/Park/Random Category Rambls: list of generated options, selected Rambl
4. Create a footprint: the user’s experience rating
5. Make your stomp: the number of Ramble points to stomp
6. Stomp Progress: both the progress bar and the time remaining on the stomp
7. Boost your stomp: the number of Rambl points to boost by
8. Past Rambls: two sample Rambls for imagined user
9. Friends’ Rambles: three sample Rambls of imagined friends
10. Your next Rambl: selected friend’s Rambl to follow
11. Add friends: imagined contact list and selected friends to add

23. Limitations/Tradeoffs
The medium-fi prototype has the following limitations:
No location tracking capability
No information storage capability
No recommendation system to suggest appealing attractions to users.
These capabilities were left of out of the medium-fi prototype and will be addressed in the hi-fi prototype, because they require building the back-end algorithms required to store and extract data from database, as well as to suggest recommendations using machine learning to the user based on the user’s observed preferences. These capabilities will be addressed in the hi-fi prototype per the assignment description.