Structures In Online Discussion Forums: Promoting Inclusion Or Exclusion?

While race and gender academic disparities have often been categorized via differences in final grade performance, the day-to-day experiences of minoritized student populations may not be accounted for when only concentrating on final grade outcomes. However, more fine-grained information on student behavior analyzed using AI and machine learning techniques may help to highlight the differences in day-to-day experiences. This study explores how linguistic features related to exclusion and social dynamics vary across discussion forum structures and how the variation depends on race and gender. We applied linear mixed-effect analysis to discussion posts across six semesters to investigate the effect of discussion forum structure, race, and gender on linguistic features. These results can be used to suggest design changes to instructors’ online discussion forums that will support students in feeling included. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.