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- W3008179626 abstract "Fully-online graduate degree programs are on the rise, generating new questions about how we conceptualize and measure student participation in the virtual classroom. To date, most studies examine participation patterns in single online and/or hybrid courses and do not take into account the demographic characteristics of online students. We develop and test hypotheses that address demographic differences in the nature and intensity of graduate student participation in online-only classrooms for a large degree program in computer science. This work is distinctive because it addresses participation across several classes and across multiple semesters. We select the Piazza forum because it is the required communication mechanism in the program that is the subject of our study. We extract Piazza discussion board activity logs from a sample of 1914 online computer science graduate students, specifically the numerical data indicating the type of access to Piazza students used. We distinguish between active (contributions), passive (viewership) participation and lurking behavior. Given the nature of the dependent variables of interest, we employ different forms of regression analysis. We use logistic regression to address the likelihood of non-participation in the online forum. We then use negative binomial regression to examine the intensity of passive and active engagement, and ordinary least squares regression to examine lurking behavior. We find that the intensity of participation varies by different demographic characteristics, including by age and by race/ethnicity, but not by gender. Our study also shows a notable impact of class size, where increasing class size is associated with decreasing levels of active participation and increasing lurking behavior." @default.
- W3008179626 created "2020-03-06" @default.
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- W3008179626 date "2020-07-01" @default.
- W3008179626 modified "2023-10-16" @default.
- W3008179626 title "Lurking and participation in the virtual classroom: The effects of gender, race, and age among graduate students in computer science" @default.
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- W3008179626 doi "https://doi.org/10.1016/j.compedu.2020.103854" @default.
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