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Thanks everyone for coming out. I really appreciate it; you guys are looking great.
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My name is Jim Jones. I am a Ruby on Rails engineer, and I work as a consultant in the San Francisco area.
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I have 14 years of experience working with Rails on and off for the past six years.
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Like many of you, I have a lot of side projects, to the point where most of them aren't completed. I haven't forgotten about some of them.
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There are times when I'm experimenting with certain images or trying to write some copy text, and I feel stuck.
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I get stuck to the point where colors start looking awkward, my writing sounds strange, or it's 2 AM, and I'm trying to be funnier than I probably am. At that moment, I can't really tell what the issue is.
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So, I sought out usability solutions. While there are many usability services available, there aren't very many that allow me to evaluate certain bits of the site.
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Most of these services are very formalized, while I wanted a quick point of feedback without needing to formalize my requirements.
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I had some prior experience with Mechanical Turk for data gathering, so I started to explore this path.
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For those who don't have a background in this, Mechanical Turk is a crowdsourcing marketplace. It allows individuals to coordinate small tasks to be performed by people.
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Imagine someone proposes a problem to an army of 1 million people, each of whom can perform a 10 or 20-second task for you. What would you use that for? These are the micro tasks that Mechanical Turk excels at.
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These tasks are generally those that computers cannot do well. Most people's experiences with Mechanical Turk involve straightforward tasks, such as tagging images and scientific surveys, usually limited to demographic data.
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I want to expand that perception and get you thinking differently towards the end of this talk. Just to clarify some terminology, when I refer to a HIT, that's a Human Intelligence Task.
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This is the task you post on Mechanical Turk, instructing the worker to do something on your behalf. The workers performing these tasks are called 'turkers,' and an assignment is something within that specific HIT.
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For example, if you want to classify an image and request 100 responses, you would have one HIT with 100 assignments. Through this process, you can take the most common classification for that image.
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The estimates for the Mechanical Turk service indicate there are millions of workers with various motivations. The most common motivation is monetary compensation, as many turkers try to supplement their income.
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However, some people are just passing the time or seeking alternative motivations, such as battling insomnia or needing extra cash. Next time you find yourself bored, explore the service; there are many engaging tasks available.
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The demographic breakdown shows that the United States dominates the service, with India in a close second. Although workers come from various countries, the majority are from the United States and India.
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In the U.S., the gender ratio is primarily female, while in India it is mostly male. The predominant age range for workers in both countries is 24 to 33 years. Surprisingly, most users have at least a bachelor's degree, indicating a high level of education among turkers.
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It's essential to understand this aspect because the quality of feedback received from educated users can be quite impressive. The average income for U.S. workers on the platform tends to be middle-class, looking to supplement their earnings.
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When working with Mechanical Turk, there are three ways to interface with the service: a basic web interface, an API for Ruby, and a command-line tool. Most people’s experience is likely limited to the basic web interface, which is a simple form builder with linear data collection.
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The simple web interface generates HTML pages served from Amazon S3. However, a powerful method is using external HITs, which allow the turkers to interact with your website directly. Your entire form can be displayed within an iframe, giving you full control over how the form is presented.
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With the Turbo gem, integration with Mechanical Turk and Rails becomes seamless. The TURK gem provides various methods for easier integration. Instead of posting data back to your server, the data gets sent straight to Mechanical Turk, which helps streamline feedback collection.
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The workflow generally involves a turker completing your assignment, with data posted to Mechanical Turk. You can retrieve this data through a rake task, import it into your models, and programmatically approve or reject it.
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Now that we've set up the basics, let's delve into more advanced cases, focusing on soliciting feedback.
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My objective is to gather meaningful feedback quickly for better development decisions. I want to skip the deployment phase entirely and get feedback as soon as possible, especially when I'm the only person working on a project.
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Sometimes, I may not want to merge changes into the master branch or deploy to a staging server, so I just want to experiment on my local development environment.
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The first tool I recommend is the Forward gem. This gem makes it easy to expose your local development instance to the outside world, providing a convenient domain for others to access.
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There’s another option called Local Tunnel, which is free, but I prefer Forward because it provides an HTTPS interface and a static domain.
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Next, we will use the TURK gem for feedback collection. After inserting the method into your application template, you can run a rake task to create a feedback study, passing in the URL provided by Forward.
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Imagine you're working on an experimental branch and want feedback. You can post your HIT to gather responses without deploying. After gathering feedback, you define a workflow to retrieve responses and analyze the results.
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I want to share feedback I've received from turkers on a project I’m working on called 5s5, a website allowing fans to bid for video chats with celebrities. The study form helper creates an overlay for collecting feedback from users.
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Some feedback from turkers has been very insightful, such as suggesting making the logo bolder or providing more information in the overlay. This feedback is valuable as it helps identify areas for improvement.
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With feedback, it's essential to focus on common threads. A few pro tips when soliciting feedback include making personal appeals rather than corporate ones, ensuring you invite negative feedback, and explaining the purpose of your project.
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Asking workers to provide detailed feedback will lead to more helpful responses. By inviting them to give their opinions, you can foster a safe environment for sharing constructive criticism.
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Now we will discuss A/B testing using the Vanity gem. This framework integrates well with Rails and helps test various aspects of your application. For instance, I set up an A/B test to evaluate different homepage images and measure user interactions.
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In this example, we tested how different images led turkers to interact with an event page. The instructions provided were open-ended, giving participants the freedom to navigate the site however they chose.
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The results were inconclusive; while one image performed better than the other, the difference wasn't statistically significant. This shows the importance of continued testing and experimentation in development.
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Now, let's address the issue of spam data. Mechanical Turk has struggled with spam submissions, with estimates suggesting as much as 40% of all submissions may be garbage. However, improvements have been made to combat this problem.
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There are a few strategies to mitigate spam, including using standard questions to validate submissions and incorporating feedback validation into your forms.
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By validating the feedback provided by turkers, you can enhance the quality of responses. Research suggests that this technique often leads to turkers re-evaluating their submissions.
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Now, let’s explore some of the surprising research findings related to Mechanical Turk that might leave you intrigued.
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For instance, increasing compensation doesn’t always lead to more accurate results. To achieve better outcomes, tasks should be checked for redundancy, meaning having multiple checks on submissions can help enhance accuracy.
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Another finding was that many tasks are completed during specific peak times, notably between 2 AM and 11 AM in the U.S. and 11:30 AM to 8:30 PM in India.
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Additionally, it’s important to note that when posting HITs, turkers often work efficiently on tasks based on their visibility in the queue. By having a higher number of posted HITs, you increase the chances of getting your tasks completed.
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Lastly, I want to highlight creative uses for Mechanical Turk, showcasing how it can be utilized beyond data collection. Many individuals are beginning to explore its potential in content creation and iterative processes.
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For example, you could create collaborative content by having turkers build off each other’s submissions for writing projects.
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I encourage you to check out my GitHub project named 'ad vote,' which simulates the Google AdWords experience without the cost. It allows you to test ad variations effectively.
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A unique project I found interesting involved having turkers draw pictures of sheep, which showcased their artistic skills while capturing their creativity.
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Additionally, there was an experiment to see how creatively turkers could rewrite news headlines. The results were often humorous and provided intriguing perspectives.
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I will now open the floor for questions. Thank you all for your attention!
