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The “Doer Effect” is the empirical phenomenon observed as a stronger correlational relationship between students who complete more activities and their course learning outcomes compared to those who complete fewer activities or watch fewer videos. In this paper, we extended prior evidence of a “Doer Effect” to investigate how doing more can be related not only to better learning outcomes but also to motivational ones. Specifically, we investigated persistence as the student’s willingness to continue working on course activities. We used secondary analyses of data from MOOC that taught Advanced Placement (AP) Introductory Java Programming to high school students using the digital textbook platform RuneStone. Although we failed to identify a doer effect in learning outcomes, our analyses do suggest that completing more activities is related to longer persistence in the course than reading more pages or watching more videos. This effect does not appear to be limited to highly motivated students. 

Promptly addressing students’ help requests on their programming assignments has become more and more challenging in computer science education. Since the pandemic, most instructors use online office hours to answer questions. Prior studies have shown increased student participation with online office hours. This popularity has led to significantly longer wait times in the office hours queue, and various strategies for selecting the next student to help may impact wait time. For example, prioritizing students who have not been seen on the day of the deadline will extend the wait time for students who are frequently rejoining the queue. To better understand this problem, we explored students’ behavior when they are waiting in the queue. We investigate the amount of time students are willing to wait in the queue by modeling the distribution of cancellation time. We find that after waiting for 49 minutes, most students will cancel their help request. Then, we looked at students’ coding actions during the waiting period and found that only 21% of students have commits while waiting. Surprisingly, students who waited for hours did not commit their work for automated feedback. Our findings suggest that time in the queue should be considered in addition to other factors like last interaction when selecting the next student to help during office hours to minimize canceled interactions.