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

Second-year doctoral student Yiwen Lin (pictured right) is lead author of an article examining the relationship between linguistic signals of social/cognitive presence and learning outcomes for male and female student in college chemistry classes.

​The article, titled “LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses”, is published in AIED 2020: Artificial Intelligence in Education.

Co-authors are fourth-year doctoral student Renzhe Yu and Assistant Professor Nia Dowell.

Lin is specializing in Teaching, Learning, and Educational Improvement. Her research interests include collaborative learning, educational technology, online teaching, and learning, diversity and equity. She is particularly interested in applying and developing quantitative methodologies to examine the social and cognitive aspects in online learning. Lin is advised by Dr. Dowell.

Yu, who is advised by Professor Mark Warschauer, 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 from the data available in various digital learning environments, with the goal of improving instructional design based on such empirical evidence.

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
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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.