You asked, now what? Modeling Students' Help-Seeking and Coding actions from Request to Resolution

Demand for education in Computer Science has increased markedly in recent years. With increased demand
has come to an increased need for student support, especially for courses with large programming
projects. Instructors commonly provide online post forums or office hours to address this massive demand
for help requests. Identifying what types of questions students are asking in those interactions and what
triggers their help requests can in turn assist instructors in better managing limited help-providing resources.
In this study, we aim to explore students’ help-seeking actions from the two separate approaches
we mentioned before and investigate their coding actions before help requests to understand better what
motivates students to seek help in programming projects. We collected students’ help request data and
commit logs from two Fall offerings of a CS2 course. In our analysis, we first believe that different types
of questions should be related to different behavioral patterns. Therefore, we first categorized students’
help requests based on their content (e.g., Implementation, General Debugging, or Addressing Teaching
Staff (TS) Test Failures). We found that General Debugging is the most frequently asked question. Then
we analyzed how the popularity of each type of request changed over time. Our results suggest that
implementation is more popular in the early stage of the project cycle, and it changes to General Debugging
and Addressing TS Failures in the later stage. We also calculated the accuracy of students’ commit
frequency one hour before their help requests; the results show that before Implementation requests, the
commit frequency is significantly lower, and before TS failure requests, the frequency is significantly
higher. Moreover, we checked before any help request whether students changed their source code or
test code. The results show implementation requests related to higher chances of source code changes
and coverage questions related to more test code changes. Moreover, we use a Markov Chain model to
show students’ action sequences before, during, and after the requests. And finally, we explored students’
progress after the office hours interaction and found that over half of the students improved the correctness
of their code after 20 minutes of their office hours interaction addressing TS failures ends.