"LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses"

Second-year doctoral student Yiwen Lin (right), fourth-year doctoral student Renzhe Yu, and Assistant Professor Nia Dowell published an article in Artificial Intelligence in Education on cognitive linguistic features significantly predicting female’s likelihood of passing an online chemistry course.

The title of the article is "LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses."
​
Lin is specializing in Teaching, Learning, and Educational Improvement (TLEI). Her research interests include educational technologies, collaborative interactions and learning at scale. She is particularly interested in applying and developing quantitative methodologies to examine the social and cognitive aspects in online learning. Dowell is serving as her advisor.

Yu is specializing in Language, Literacy, and Technology. His research interests include learning analytics, learning sciences, instructional design, and computational modeling. He is focusing on advanced computational methods to model and decipher student learning processes. Yu is advised by Professor Mark Warschauer.

Dowell’s research interests encompass cognitive psychology, discourse processing, group interaction, and learning analytics, with focuses on using language and discourse to uncover the dynamics of socially significant, cognitive, and affective processes. She applies computational techniques to model discourse and social dynamics in a variety of environments including small group computer-mediated collaborative learning environments, collaborative design networks, and massive open online courses (MOOCs).

Abstract

Women are traditionally underrepresented in science, technology, engineering, and mathematics (STEM). While the representation of women in STEM classrooms has grown rapidly in recent years, it remains pedagogically meaningful to understand whether their learning outcomes are achieved in different ways than male students. In this study, we explored this issue through the lens of language in the context of an asynchronous online discussion forum. We applied Linguistic Inquiry and Word Count (LIWC) to examine linguistic features of students’ reflective posting in an online chemistry class at a four-year university. Our results suggest that cognitive linguistic features significantly predict the likelihood of passing the course and increases perceived sense of belonging. However, these results only hold true for female students. Pronouns and words relevant to social presence correlate with passing the course in different directions, and this mixed relationship is more polarized among male students. Interestingly, the linguistic features per se do not differ significantly between genders. Overall, our findings provide a more nuanced account of the relationship between linguistic signals of social/cognitive presence and learning outcomes. We conclude with implications for pedagogical interventions and system design to inclusively support learner success in online STEM courses.