I want to pick the best hammock spot, so I would like to see available options.
I want to feel safe in my hammock, so I would like to see potential hazards.
I want to hang my hammock correctly, so I would like to see how it should be hung.
I want to remember previous spots, so I would like the ability to to view my trip history.
Looking for guidance in where and how to hang their hammock.
Searching for spots quickly if their favorite spot is taken or when they don’t have much free time.
Comparing sites for multiple hangs and checking for nearby hazards.
The experience of finding a hammock spot should not disrupt the experience of being in nature.
Complement the act of hammocking — laid back and functional, with a minimal UI factor.
Design for a first-person perspective with an open discovery viewpoint. Virtual components should be easily and quickly identifiable against variable environmental backdrops. Optimize for the range of potential spots to be limited to a five meter radius.
Measuring engagement is a cornerstone of experience design, and every unique design problem has unique participation measurement needs. For this project I’m focusing on the types of engagement that augmented reality brings to a user experience: immersive and educational [note]https://ieeexplore.ieee.org/document/6681863/ Section 4.2 Design Strategies[/note].
Flows and feedback that are non-linear and encourage further inspection, like encouraging the user to explore natural environments for hammock spots.
Content that requires users to exchange ideas, like prompting the user to share information about known hazards and dead trees, and later hammock spots.
Content that allows for users to be engaged at a constant level.
May include showing additional information about the environment to the user, and learning about the types of information users might want to see in a forest context.
Education is part of the overall engagement strategy because user research revealed a learning curve around hanging hammocks, a stage of trial and error most new hammockers don't anticipate. This experience-gathering stage mirrors an experiential learning [note]https://books.google.com/books?hl=en&lr=&id=jpbeBQAAQBAJ&oi=fnd&pg=PR7&ots=Vn4VpU0TQh&sig=6j2Bm4ZsGj4dHpz-USZZ5riVFIY#v=onepage&q&f=false[/note] process, as highlighted below:
EXPERIENTAL LEARNING PROCESS
Start with a concrete experience
Basis for observation and reflection
From there to creating theories
Then testing results
Outcome informs new experience
HAMMOCK HANGING PROCESS
Spends enjoyable time in a hammock.
Wants to recreate experience.
Tries different distances and angles.
Hangs hammock with applied theory.
Develops a new model for spatial details.
By understanding the user's learning model, we can explore opportunities to improve task success. For example, applying contextual learning concepts [note]https://ieeexplore.ieee.org/document/6681863/ Section 6.3 Contextual Visualization[/note] to an existing model may increase immersion and enjoyment.
CONTEXTUAL LEARNING PROCESS
Link new concept to familiar one
Explore, discover, invent
Use concepts in realistic exercises
Chance to share and respond
Use new knowledge in different context
HAMMOCK HANGING PROCESS
Virtual hammock in the app connects to real hammock behavior
User sees hang options and is motivated to explore surroundings
User is provided with an interactive guide to assist with hammock hanging
User is asked to share hazardous trees with other hammockers
User experiments with hanging hammocks in non-forest settings
Plan for these stages as guideposts toward successfully measuring adoption and retention.
The nature of the experience produces an unintentional variable reward loop [note]https://en.wikipedia.org/wiki/Reinforcement#Intermittent_reinforcement_schedules[/note], since there’s no way of knowing how many hammock spots are in a location until the user takes the action of scanning there.
The challenge for the UX component is to understand the variable range of spots and better respond to any outcome. At the moment, our parameters in any scan fall between [no matches] and [too many to visualize]. How do we respond when there are no matches for the user? And where there are too many? How do we define “too many”? One of the first tentpole metrics we'll want to gather is number of matches per scan.
Show you where to hang a hammock by scanning the trees around you and calculating ideal hang spots based on base weight and hammock size.
HANGING A HAMMOCK