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Do you have questions about how to navigate your relationship with your PhD advisor?
Or, do you want to help other PhD students who are facing challenges?
If so, this system and study are for you!

This research study is only for University of Michigan School of Information and CSE PhD students.


We ask you to first read the consent form and fill it out. The next page has the actual sign-up page!


Moa: A System for Enabling Communication Between PhD students About Navigating PhD Advising Relationships
eResearch ID: HUM00258907

You are invited to participate in a research study for evaluating Moa, a system for enabling consentful communication between PhD students about navigating PhD advising relationships.
We ask participants to log in 2 times per day for one week. The study is followed by a short survey ($10 for 5 questions) and an optional interview ($15/30 minutes, $30/hour). During the study, we ask you to regularly check Moa to see if any posts interest you. We also encourage you to post or comment using Moa's consent boundary features.

Compensation:
  • Among participants who posted or/and commented legitimately, we will draw a raffle to select 5 participants to receive a $100 gift card or a check. The likelihood of winning $100 is proportional to the number of posts/comments a participant creates legitimately.
  • Participants who fill out the survey (5 questions) will be compensated $10.
  • Participants who also decide to participate in the follow-up interview will be compensated at a $30/hour rate ($15 for 30 minutes).
Benefits of the research:
Participants could directly benefit by connecting with other PhD students and collectively navigating their advising situation. Our findings have broader contributions to the design of social computing systems grounded in consent. The results will directly inform a software for enabling communications between people who experience challenges in relationships where there are power differentials, especially in the context of academia.

Risks and discomforts:
The risk would be sharing information about one's advising situation with another PhD student on the system (Moa) in an unexpected way. However, this is highly unlikely as the system emphasizes anonymity—users can set different usernames per discussion and change their default username anytime, and the system has various degrees of visibility and “consent boundary” features that the PhD students can use. That is, Moa lets users set the visibility of a post or comment to PhD students with certain kinds of backgrounds, identities, and prior experiences. Read more here: https://moa.systems/about/

We will protect the confidentiality of your research records by following the below procedures.
1. Moa’s servers are managed by the UMSI ITS team and Fly.io, which has signed a Data Protection Agreement with the UMSI ITS team. Jane technically has access to all of the information you submit, including posts. But, this is ONLY to verify PhD students signup and prevent abuse of the system. Other than 1) checking signups and 2) handling cases of someone alerting me about any problematic user behaviors (e.g., harassment, uncivil comments), Jane will NOT look into the database at a user-level. Especially as someone who went through multiple advisor changes, Jane is aware of and empathizes with the sensitivity of these issues. Jane will be scanning all posts and comments without looking at the user information, unless the content is alarming (e.g., discriminatory comments, harassment towards other users). Note: Kentaro Toyama, Jane’s advisor and only collaborator on the project, will NOT have any direct access to the system.

2. For the follow-up interviews, we will record the interview (either using iPhone/Macbook’s recording app or Zoom), only if you feel comfortable with it.

3. For those who are compensated, only your name, mail address, or/and email address will be collected for study compensation purposes only.

4. When you use Moa, we will log your actions about how you use Moa’s features, including consent and privacy settings – but they will only be used for high-level aggregate analysis. For the final paper, we will NOT include any individually identifiable information.

5. Once the field study and the analysis of the data are all complete, we will immediately discard all data on the server. We will not share the collected information with other researchers who are not on the research team.

If you have questions about this research study, please contact:
Principal Investigator: Jane Im (imjane@umich.edu)
Faculty Advisor: Kentaro Toyama (toyama@umich.edu)