Hitting the Books & The Drawing Board
Last week I had the thought that I would jump directly into starting to build my app post the last prototype which examined the UI/look & feel of the project as well as an IRL demo of my proposed virtual interaction.
It became clear to me after playtesting the last prototype last week that more research and work was needed. I had several key problems that had to be addressed and tested before starting to code. So for this week, I decided to keep watching the Lynda videos on how to do basic app layouts in Swift. I am taking a full Swift online development course simultaneously to these prototypes because I really like to start these lessons from the beginning and learn by building up when I can, rather than just taking the chunks that I need and not linking them. Since my final for my Creative Coding iOS class will also be an app in Swift, this felt like a good use of project time as I move forward through the last weeks of the semester.
So instead of starting to code the interaction, I decided to do two things and answer 3 big questions that were posed to be (and that I started to realize on my own as well).
- The idea of just clearing a mirror to see yourself isn’t a compelling enough interaction for the main goal of the app. What could I do instead?
- How can I replicate the types of questions/data collection methods (and associated tropes) used today so that people using my app will feel a familiar sense of process and user-experience, but bring together the results in a creepy way that provokes self-reflection from my audience?
- How and when should I reveal my collection of this data in poignant – yet integrated – manner and what precedents might inform thinking about this delivery?
With these questions in mind, I started to do some serious research on different types of data mining techniques.
One piece that I thought broke down some of the main categories of how/why data is collected was a Huffington Post article, Everything You Wanted to Know about Data Mining but were Afraid to Ask. This article introduced me to some directions/methodologies for data collection and defined the goal of data collection as
Categories of Data Mining (cited from above article)
- Anomaly detection: In a large data set it is possible to get a picture of what the data tends to look like in a typical case. Statistics can be used to determine if something is notably different from this pattern. For instance, the IRS could model typical tax returns and use anomaly detection to identify specific returns that differ from this for review and audit.
- Association learning: This is the type of data mining that drives the Amazon recommendation system. For instance, this might reveal that customers who bought a cocktail shaker and a cocktail recipe book also often buy martini glasses. These types of findings are often used for targeting coupons/deals or advertising. Similarly, this form of data mining (albeit a quite complex version) is behind Netflix movie recommendations.
- Cluster detection: one type of pattern recognition that is particularly useful is recognizing distinct clusters or sub-categories within the data. Without data mining, an analyst would have to look at the data and decide on a set of categories which they believe captures the relevant distinctions between apparent groups in the data. This would risk missing important categories. With data mining, it is possible to let the data itself determine the groups. This is one of the black-box type of algorithms that are hard to understand. But in a simple example – again with purchasing behavior – we can imagine that the purchasing habits of different hobbyists would look quite different from each other: gardeners, fishermen and model airplane enthusiasts would all be quite distinct. Machine learning algorithms can detect all of the different subgroups within a dataset that differ significantly from each other.
- Classification: If an existing structure is already known, data mining can be used to classify new cases into these pre-determined categories. Learning from a large set of pre-classified examples, algorithms can detect persistent systemic differences between items in each group and apply these rules to new classification problems. Spam filters are a great example of this – large sets of emails that have been identified as spam have enabled filters to notice differences in word usage between legitimate and spam messages, and classify incoming messages according to these rules with a high degree of accuracy.
Looking at the above categories, one thing that really stood out to me from this article was the quote:
Prototype 3 | Steamy Selfie
Taking this research and feedback from last week into account, I set out to do a couple of things with this prototype.
- Reframe the app as a drawing/sharing picture app vs. a mirror app. The goal of the app becomes to draw on steamy window over a selfie (taken by user and steamed up by app) to make fun pictures and share with friends.
- Simplify the user interface and make it very clear for the user how to access the “defogger” menu, necessary to power the app.
- Rethink the questions and how they are structured vs. payments to incorporate my research on data mining and behavioral psychology better into the structure.
- Create a hypothetical “profile” that may be collected by my information to help inform the idea for the reveal at the end as well as the questions and categories.
I began by brainstorming all of the ways I could reframe this app as more than just clearing a mirror (a utilitarian mirror app). I knew that I wanted to keep my idea of a mirror or screen, also the metaphors of steam/fog/obfuscation vs. clarity/shadows/hidden vs obvious so I thought of a couple of new ideas that I ran by friends for some feedback.
- Steamy Dashboard Racing Game
- Fog Game where you have to clear fog to… navigate maze, not get killed etc.
- Steamy Mirror Picture App where you draw on a steamy mirror and share photos with friends.
User Interface Changes
I had several feedback items from last week regarding issues with my user interface. The biggest was that it was not obvious how to access the defog questions (from the settings menu) with a defog icon on the actual app that did nothing.
Another big issue raised was that the payment system really didn’t make sense. Most apps let you buy the full version for a price, but don’t charge actual money in-app, that is more a convention of games and the idea of “crystals/energy/power” needed to move forward without having to wait for them to refill naturally over time.
Finally, the symbols I included about defog levels “hand wipe”, “towel wipe” “squeeqee wipe” were not connecting for people since the questions were labeled in tiers. It also didn’t connect with the progress bar like icon indicating how much defog was left. I need to clearly connect what swipe is, what defog is and how it relates to swipes, and how one goes about getting defog.
So for this new prototype, I attempted to address these issues. I made it so that the “defog icon” which is the defroster symbol found in most cars/trucks now pulls up the defog menu. I also moved around where things are and also eliminated other areas completely such as the “pay for swipes” option. Now users can just pay for the full version of the app which would not require providing anything to draw on the steamy mirror, it would just work.
I also decided to eliminate the third (most intrusive) tier of questions entirely. While these questions were interesting, they didn’t tie directly to any data method collection tecniques currently in play, and I realized it would be more powerful if I used insights combined from Level 1 and 2 questions about preferences and demographics to instead infer answers that would come out of level 3.
However, I really wanted to keep in the idea of linking profiles since this is a super-common activity that allows data miners to personally identify us in many cases. By choosing to log in with linked accounts instead of creating new information, we are basically handing them the connections ourselves. To represent this, I instead decided to use a “multiplier”. If a user links their Facebook, Twitter, Instagram or LinkedIn profile, they automatically get 2x the amount of defog/swipes for information provided. This is supposed to somewhat symbolically represent that providing profile information gives continuous additional value to the interactions in the app, just like it would give continuous value in helping data collection techniques tie results directly to an actual person easily. They could probably do it anyway with enough information, however by linking your profile you make it twice as easy for them, so it should be twice as easy for you to use my app.