How Twincode Uncovers Gender Bias in Remote Pair Programming | HackerNoon
Briefly

During our data analysis, we excluded any dialog messages disclosing gender to investigate the impact of perceived partners' gender on students' responses.
The control group showed ideally lower response distance than the experimental group, where perceived partners' gender varied, indicating possible influences on student performance.
Using Cronbach's alpha and the Kaiser criterion ensures our questionnaire data's internal consistency before we analyze the differences in dependent variables.
Our mixed-model ANOVA investigates potential interactions between perceived partner's gender and subject's gender, shedding light on gender dynamics in educational settings.
Read at Hackernoon
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