Master’s students who are struggling to finish their thesis need organizational and external motivation tools to complete it in a timely manner.
This problem space originated from my own experience in struggling to complete my master’s thesis. I learned early on that I respond best to external deadlines, and the members of my cohort reported feeling the same. My solution was to impose external deadlines and consequences for failure to complete these deadlines on myself. For example, I set a deadline and immediately messaged an adventurous friend that I would take her skydiving if I did not meet this deadline.
My intense aversion to skydiving proved to be an effective tool, and I did reach this deadline on time. I realized others responded to this method of motivation as well; an NPR podcast episode of Radiolab told the story of an activist smoker, who authorized her friend to donate 1,000 USD to an organization with opposing views if she ever had another cigarette and she had not smoked in fifteen years.
This identification of a personal solution got me very interested in this problem in the context of a master’s thesis. Upon further research, I discovered that this was a widespread problem that many simply to not overcome.
It was important during the user research that I set aside my own bias and assumptions to truly understand the whole problem, especially because I came to this project with a solution in mind. During the initial stages of planning, I deeply considered the underlying issue that would lead me to seek accountability from my peers to complete my master’s thesis. I identified some key factors initially, specifically time, motivation, and follow through, and aimed to dig deeper using my cohort for user research.
I constructed my survey and user interview questions with two goals in mind: to understand the biggest obstacles to completing a thesis, and to identify the most effective methods of motivation utilized by students in their pursuit of the thesis. In total, I conducted 3 user interviews, and got 7 responses to my survey from users in my own master’s cohort.
It is important to note here that while the sample group is likely an accurate representative of the target group, the small number of responses presents a potential limitation in validity of data. Nonetheless, the research revealed some compelling insights which I used to compile my product specification document.
Research confirmed some of my assumptions and challenged others. Some key insights are as follows:
· While most users surveyed reported responding better to extrinsic vs. intrinsic motivation, the distribution was much closer than anticipated. 57.1% of users reported extrinsic motivation as the most effective method of motivation, and 42.7% preferred intrinsic motivation.
· The biggest obstacle to completing the thesis was more dispersed than anticipated, however the two most selected obstacle selected were predictably, lack of time and motivation in general
· When asked to freely type what methods they typically use to motivate themselves to complete the thesis, almost all users reported envisioning the future (i.e. how happy they would be when their thesis is finished) and others noted more tangible methods such as setting up tasks and deadlines.
Landing on the Solution
The solution to this problem is a tool which supports students in establishing tasks and deadlines and utilizes rewards/punishments enforced by peer groups to ensure their completion. While I have an idea of how this solution will be enacted, I’m looking forward to working with a team to establish the “how” of the solution.
Product Manager Learnings:
I was able to apply and sharpen skills I gained during my master’s program. For example:
- Survey Writing: constructing questions that are clear, concise, and elicit the desired data.
- Data Collection: I began the program quite comfortable with conducting interviews as I had conducted, transcribed, and analyzed ten hour-long qualitative interviews for the data collection of my thesis. I learned how to utilize these skills as well as understood the differences between academic and problem-related interview techniques and experiences.
- Data Analysis: analysis of quantitative and qualitative data to draw strong numerical conclusions and tell stories about a problem space.
I now understand the importance of breaking down a large problem into small, digestible pieces. Starting with the background, including a clear problem statement, and incorporating goals, user stories, and scenarios to communicate the problem effectively. It’s an important skill to be able to focus each of these areas clearly without getting ahead or veering towards the solution. I learned a new way of approaching a problem in an elaborate way.
Overall, the greatest thing I gained was a comfort with the problem space. I was able to shift my thinking to live in the problem space and go from there. To really understand the actual problem and not let my own biases or assumptions about the solution influence research.