This study investigates how certain teacher characteristics—specifically, math anxiety and confidence in teaching mathematics—and school-context features are associated with teachers’ behavioral engagement patterns in an online teacher professional learning platform. To this end, we applied frequent sequential pattern mining to elementary teachers’ log data collected from an online professional learning platform, the Virtual Learning Community (VLC), and linked it with survey data. Results indicate that teachers with higher levels of math anxiety were significantly more likely to remain within a single section of VLC rather than navigate across multiple sections (b = -0.764, p <.001). Additionally, this exploratory engagement was positively associated with teachers’ self-reported instructional practices (b = 0.743, p <.001). This finding indicates that teachers who navigated across multiple sections of VLC were more likely to perceive improvements in their instructional practice. Our research contributes to empirical evidence on how individual differences contribute to diverse patterns of participation in online professional learning, and it discusses practical implications that offer insights for designing teacher-specific support strategies in these environments.

This study explored gendered language used in an online chemistry course’s discussion forums, to understand how using gendered language might help or hinder learning outcomes, while considering the goal of various posting structures required in the course. Findings revealed that although gendered-language use did not differ between men and women, gendered forms of language were widely used throughout the forums. The use of gendered language appeared strategic, however, and reliably varied by the goal of the discussion post (i.e., posting a solution to a homework problem, asking a question, or answering a question). Ultimately, gender, language and posting type were found to be related to final grade.

Little is known about the online learning behaviors of students traditionally underrepresented in STEM fields (i.e., UR-STEM students), as well as how those behaviors impact important learning outcomes. The present study examined the relationship between online discussion forum engagement and success for UR-STEM and non-UR-STEM students, using the Community of Inquiry (CoI) model as our theoretical framework. Social network analysis and nested regression models were used to explore how three different measures of forum engagement—1) total number of posts written, 2) number of help-seeking posts written and replied to, and 3) level of connectivity—were related to improvement (i.e., relative performance gains) for 70 undergraduate students enrolled in an online introductory STEM course. We found a significant positive relationship between help-seeking and improvement and nonsignificant effects of general posting and connectivity; these results held for UR-STEM and non-UR-STEM students alike. Our findings suggest that online help-seeking has benefits for course improvement beyond what can be predicted by posting alone and that one need not be well connected in a class network to achieve positive learning outcomes. Finally, UR-STEM students demonstrated greater grade improvement than their non-UR-STEM counterparts, which suggests that the online environment has the potential to combat barriers to success that disproportionately affect underrepresented students.

Previous research has shown that students from underrepresented minority groups tend to receive lower grades in online classes than their peers, especially in science-focused courses. We propose that there may also be benefits to online courses for these students (e.g., opportunities for peer discussions where minority status is less salient), though little is currently known about these potential benefits. We present a new perspective on learning outcomes by measuring improvement, rather than grades alone. In learning management system data from seven semesters of an online introductory science course, we found that students from underrepresented minority racial groups were indeed less likely to receive high grades, and scored lower on exams; however, their exam scores improved throughout the semester a similar amount compared to their peers. We also compared improvement to students' behaviors, including exam submission times and forum usage, finding that these behaviors were related to improvement. Finally, we also briefly discuss implications of these findings for reducing inequalities in education, and the possibilities for underrepresented minority students in online STEM education in particular.

Massive open online courses (MOOCs) continue to see increasing enrollment and adoption by universities, although they are still not fully understood and could perhaps be significantly improved. For example, little is known about the relationships between the ways in which students choose to use MOOCs (e.g., sampling lecture videos, discussing topics with fellow students) and their overall level of engagement with the course, although these relationships are likely key to effective course implementation. In this paper we propose a multilevel definition of student engagement with MOOCs and explore the connections between engagement and students' behaviors across five unique courses. We modeled engagement using ordinal penalized logistic regression with the least absolute shrinkage and selection operator (LASSO), and found several predictors of engagement that were consistent across courses. In particular, we found that discussion activities (e.g., viewing forum posts) were positively related to engagement, whereas other types of student behaviors (e.g., attempting quizzes) were consistently related to less engagement with the course. Finally, we discuss implications of unexpected findings that replicated across courses, future work to explore these implications, and relevance of our findings for MOOC course design.

Massive Open Online Courses (MOOCs)—in part, because of their free, flexible, and relatively anonymous nature—may provide a means for helping overcome the large gender gap in Computer Science (CS). This study examines why women and men chose to enroll in a CS MOOC and how this is related to successful behavior in the course by (a) using k-means clustering to explore the reasons why women and men enrolled in this MOOC and then (b) analyzing if these reasons are related to forum participation and, ultimately, persistence in the course. Findings suggest that women and men have different reasons for taking this CS MOOC, and they persist at different rates, an outcome that is moderated by forum participation.