Using Dialogue Analysis to Predict Women's Stress During Remote Collaborative Learning in Computer Science

The computer science education community strives to improve equity and representation within the field, yet the proportion of women earning CS bachelor's degrees in countries such as the US remains low. In addition to recruitment and retention initiatives that support women, we need to better understand women's experiences within CS. This paper makes a novel contribution toward this effort by examining women's self-reported stress during remote collaborative programming with a peer. Women reported significantly more stress than men, so we analyzed the women's collaborative dialogues and identified the most common dialogue acts and sequences of dialogue acts. We used these dialogue acts to predict women's stress and found six significant patterns of dialogue. Women reported less stress with higher frequencies of offering suggestions, having their partner provide explanations, and having their own rapport-building messages reciprocated by their partner. In contrast, women reported more stress with higher frequencies of their own explanations, having their partner answer their questions, and having their partner send a rapport-building message that they reciprocated. Understanding the nuances of these experiences allows us to make better predictions of when women might be feeling stressed and what we might be able to do to relieve these feelings. Improving women's CS experiences holds the potential to, in turn, improve gender equity within CS.