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

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.

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

“…However, these forums also present challenges due to the amount of data that is generated. Being able to efficiently mine this data for context, understanding and patterns is a challenge that can be addressed through the use of text mining, specifically automated text analysis techniques and tools (Dowell, Graesser, & Cai, 2016; Gardner & Brooks, 2018; Kovanović et al ., 2017; Krippendorff, 2019; Lin, Yu, & Dowell, 2020; Moore et al ., 2019).…”

It’s Not That You Said It, It’s How You Said It: Exploring the Linguistic Mechanisms Underlying Values Affirmation Interventions at Scale

“…The dictionary itself has been revised and validated over the course of two decades, and the most recent version consists of 6,400 English words/word stems, covering a range of social and psychological constructs such as affect, cognition, and biological processes (see Pennebaker et al, 2015, for details). Currently, LIWC is one of the most popular and reliable programs for text analysis available; it has been utilized in hundreds of studies across the social sciences, including psychology, education, sociology, communication, political sciences, and economics (Borowiecki, 2017; Boyd et al, 2020; Cade et al, 2014; Dowell, Windsor, et al, 2016; Kacewicz et al, 2014; Lin et al, 2020; Newman et al, 2008; Pennebaker & Chung, 2014; Pennebaker et al, 2014). A total of 29 linguistic variables from six LIWC categories were included in the analysis.…”

“…Advances in artificial intelligence methods, such as NLP (Kao & Poteet, 2007), have made it possible to automatically (a) harness vast amounts of educational discourse data being produced in technology-mediated learning environments, (b) quantify aspects of human cognition, affective, and social processes that (c) would otherwise not be possible or extremely time-consuming for human coders to capture, given the multifaceted characteristics of human discourse. Indeed, NLP and automated text analysis approaches have proven quite useful in quantifying and characterizing psychological, affective, cognitive, and social phenomena from a learner-generated discourse (Bell et al, 2012; Cade et al, 2014; D’Mello et al, 2009; D’Mello & Graesser, 2012; Dowell et al, 2017, 2019, 2020; Dowell & Graesser, 2015; Eichstaedt et al, 2018; Kern et al, 2020; Lin et al, 2020; McNamara et al, 2014; Schwartz et al, 2013; Tausczik & Pennebaker, 2010; Zedelius et al, 2019).…”

Letters of recommendation: Nuanced bias or useful affirmation?

“…These categories include ability (ie, competent), communal (ie, caring), grindstone (ie, hardworking), standout (ie, exceptional), teamwork (ie, works well with others), gender (ie, lady), research (ie, research, manuscript), and leadership (ie, leader). Similar linguistic approaches to education and medicine have been broadly applied, allowing for insights into a diverse array of inquiry, including how gender might affect STEM (science, technology, engineering, math) learning, 4 how the online health communities interact with regard to identity and cancer diagnosis, 5 dissemination of information regarding infectious outbreaks, 6 and how media covers bullying. 7 One of the most used tools in these assessments is the linguistics inquiry and word count (LWIC; available at: http://www.liwc.…”