Month: March 2018

Based on the user feedback from the second iteration (Annex 13) some elements were changed (Fig. 1).

• Camera icon added to the first screen to eliminate an extra click.
• Text input – Added example interactions (Conversation Suggestion Pillows) on top of the bottom row to educate users of the possibilities of interactions with Rockie. This is based on the Google Assistant chatbot (Fig. 2).
• Text input – Modified mental model as users expect to add ingredient by ingredient instead of adding a list of items in one single line.
• Photo input – After taking a picture of the food, the user can press “Quick Add”, if the user did not choose this option on the first screen user was locked within the ingredients selection list. That was changed to allow users to skip.
• Photo input – Items on the food selection process became clickable.
• Photo input – Removed the swipe to the right to add to a list on top. This interaction is found in other apps but the usability becomes poor if the list of items is too long making the items to be selected disappear from the first fold of the screen.
• Voice input – Removed the hold to send button.
• Voice input – Added visual feedback when chatbot is expecting the user to talk.
• Voice input – Conversation Suggestion Pillows was added to allow the user to choose between a full voice interaction or choose a common option.

  

  

After these modifications, a guerrilla testing was made to validate some of the proposed solutions, and a video of the final prototype was produced (Fig. 3).

Figure 3. Video of the full prototype.

The architectural information was reviewed (Fig. 3) and compared with original structure (Fig. 4).

Despite the usability issues encountered during tests, most of the errors occurred because the prototypes supported one single mental model.  At this stage, the team started to reflect on different mental models, input entries and different interactions. A mapping of the possible interactions with Rockie was built (Fig. 5).

Finally, while Rockie fits Jane, it might not be suitable for all users. The team started to examine further bot interactions, different characters, bot personas (Fig. 6) and visual feedback (Fig 7).

Evolution of elements

An evolution of the elements throughout this process was mapped (Fig. 8).

Figure 8. The evolution of UI elements throughout the process.

UX Success

Looking back at the goals and the problem statement proposed initially, the pain points were solved during this project (Fig. 9). Inputting food became an easier process, the various input methods fit the user’s lifestyle, navigating and exploring information became easier and more accessible. A diary study would be necessary for further developments to verify if the proposed solutions enticed users to continue using the app for longer periods.

This project was a huge learning curve for all researchers in the team. At the initial stages, it was defined that this would be a consultancy work, the idea was to treat as one of the health & fitness apps approached us to write a report. The plan worked.

Weeks before starting this project, a report about user research methods guided the team to take right decisions when determining what were the user research methods necessary to achieve the goals of this project. Even with the correct research methods, the lack of a holistic view of the whole project meant that the questionnaire failed to answer questions about the target audience. The solution was to send another questionnaire focused solely on MyFitnessPal users. The mistake helped the team to consolidate the knowledge it had about the users, and the Persona was constructed with a solid foundation.

Another relevant aspect of the user research for this project was the triangulation of information. This project, because of its nature, proved that user behaviours and attitudes can be different. Users mentioned that they have long-term goals, but in reality, they have short-term goals triggered by life events. Users also indicated the ease of use of MyFitnessPal, but the contextual inquiry detected that users have trouble performing simple tasks or finding functionalities within the app.

Working remotely proved challenging in some aspects, which were not thought they could be problematic at first. Online tools like Boardthing allowed the team to collate and collaborate. The Skype calls were crucial to enable the team to talk over elements on Boardthing. This process proved that the user research can be done working remotely. On the other hand, prototyping proved to be a different situation, the lack of collaboration tools within Sketch meant that there was a disconnection between the talks and the screens produced. Another prototyping tool might have been better for a team where its members are far apart. Besides that, the team members were happy to have learned Sketch, I included. As Sketch is a leading prototyping tool, the decision to go with it was probably overlooked.

The screens were designed following a thought process that was not correct. Users wanted to add ingredient by ingredient, the same way they operate on the current MyFitnessPal. Over time this mental model could change, but that was out of the scope for this project. Because of the limitations of the prototyping, it was difficult to predict every interaction the user might have with a chatbot. The next step for this project would have been to give the user interaction with a fake chatbot via Whatsapp, one of the researchers would control the other end. This step would allow better insight into the user’s mental model.

Working with users interacting with chatbots was a vast unknown for the team. Stripping down the UI did not mean less work, it meant more research in areas that were entirely new for everybody in the group.

Contributions

Looking back at the project, everybody in the team contributed in all aspects of it; everybody participated, opinionated, discussed and interviewed users. These discussions allowed us to explore different areas and to view from different angles.

A participation scale was designed (Fig. 1) and the scores were assigned to each area of the project (Fig. 2).

Rating	Avaliation	Notes
1	No Participation	My participation was non-essential for this part of the project.
2	Poor Participation	My contribution was below of the efforts of my teammates.
3	Medium Participation	My contribution was at the same level as my teammates.
4	Good Participation	My contribution was important for this part of the project.
5	Excellent Participation	My contribution was essential for this part of the project.

Figure 1. The participation scale.

Area	Participation Rating
Quantitative Research	5
Perception Test	5
Qualitative Research	4
Heuristic Evaluation	5
Information Architecture	3
Problem Statement	4
Demographics	5
Personas	3
Task Analysis	4
Scenarios	3
Storyboards	3
Competitive Analysis	5
Prototyping	3
User Testing	4
Redesign	5

Figure 2. The participation scale was given to each area of the project.