Going over the cliff: MOOC dropout behavior at chapter transition

Distance Education · 2020 · 40 citations

• Computer Science
• Mathematics education
• Computer Science

Participants’ engagement in massive online open courses (MOOCs) is highly irregular and self-directed. It is well known in the field of television media that substantial parts of the audience tend to drop out at major episodic, or seasonal, closures, which makes creating cliff-hangers a crucial strategy to retain viewers (Bakker, 1993; Cazani, 2016; Thompson, 2003). Could there be an analogous pattern in MOOCs—with an elevated probability of dropout at major chapter transitions? Applying disjoint survival analysis on a sample of 12,913 students in a popular astronomy MOOC that built participants’ cultural capital (hobbyist pursuits), we found a significant increase in dropout rates at chapter closures. Moreover, the latter the chapter closure was positioned in the course sequence, the higher the dropout rate became. We found this pattern replicated in a sample of 20,134 students in a popular computer science MOOC that introduced participants to programming.

The Intersection of Being Black and Being a Woman

ACM Transactions on Computing Education · 2020 · 89 citations

• Sociology
• Political Science
• Gender studies

Computer science (CS) has been identified as one of the fastest-growing professions, with demand for CS professionals far outpacing the supply of CS graduates. The necessity for a trained CS workforce has compelled industry and academia to evaluate strategies for broadening participation in CS. The current literature in CS education emphasizes the importance of social relationships and supports for individuals from underrepresented groups. Unfortunately, this literature has largely been limited to either the exploration of issues of women or that of underrepresented racial/ethnic groups. These limited views generalize characteristics of specific underrepresented groups without considering intersections between these groups. This quantitative study ( n = 3,206) addressed that shortcoming by leveraging inferential statistical methods to examine (i) the similarities and differences between the social CS-related experiences of Black women, Black men, and non-Black women in the United States; (ii) the relationship between these experiences and CS career choices; and (iii) the activities during which significant social experiences might occur. The results indicate that Black women's social experiences are often different from the experiences of both Black men and non-Black women. In particular, both Black men and non-Black women had more CS friends than Black women, whereas having these friends was more significant for the CS career choice for Black women. Introductions to CS in school, before college, were negatively related to career choice for all groups, whereas home support was positive for both Black women and men. This work suggests that considering intersectionality is important to understanding the needs of different individuals, as well as the importance of social supports for persistence in CS.