Most of my week was focused on completing my first course 2 autograder as I had to spend a significant amount of time debugging my test cases. Through this process, I was able to learn a lot more about different pytest functionalities and the sensitivity of pytest syntax. By the end of the week, I was able to debug and complete my auotgrader, submitting it for the next step in our building process, peer review. I have picked up another course 2 autograder for a lab assignment on debugging functions. This autograder will pose its own set of challenges because it includes many student submission files, something we have not yet had to take into account. In terms of the research questions I worked on last week, this week I was able to sit down with my mentors and refine my potential questions. In addition, we also identified what data would need to be collected for each question, how feasible it is to collect this data, and how the data collection could be implemented. For some inspiration regarding my research questions, I also read a research paper on data analysis done for an introductory CS Massive Open Online Course (more details on the paper are highlighted below).

“Experience Report: Designing Massive Open Online Computer Science Courses for Inclusion” by Sophia Krause-Levy, Mia Minnes, Christine Alvarado, and Leo Porter

Massive Open Online Courses (MOOCs) pose a new opportunity for wide-scale learning that is more cost-effective. However, these courses generally seem to have low completion rates and lack gender and racial diversity. This paper highlights a study aimed to design a MOOC that included a variety of additional resources that reflected in-person learning styles in the hopes that this would increase completion rates and student diversity. This newly designed MOOC showed a higher percentage of women enrollment, higher rates of course completion for both men and women, and a slightly smaller gap between completion rates for men and women.