Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students’ experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors. © The Author(s) 2024.

This paper describes the results of surveys and interviews from over 1800 students in five large STEM classes at a research university when classes abruptly moved online for spring quarter 2020 due to the COVID-19 pandemic. We examine how students’ expectations compared to their realities at the end of the quarter; what factors impacted their spring 2020 quarter; and students’ academic sense of belonging, self-efficacy, cost of engagement, and effort regulation in spring 2020. We contextualize students’ experiences of emergency distance learning (EDL) through quantitative comparisons to previous quarters, open survey responses, and interviews. We also examine heterogeneity with respect to race/ethnicity, gender, first-generation college students, and students from low-income families. We find that there are reasons to expect increased achievement gaps post-EDL, but we also find examples of resiliency and improved self-regulated learning that will be life-long assets for students. Our goal in this paper is to use exploratory findings from one particular context to help identify potential ways to disrupt the reproduction and deepening of educational inequality exacerbated by the crisis, and educational opportunities in these unconventional times. © 2022 Taylor & Francis Group, LLC.

The importance of online learning in higher education settings is growing, not only in wake of the Covid-19 pandemic. Therefore, metrics to evaluate and increase the quality of online instruction are crucial for improving student learning. Whereas instructional quality is traditionally evaluated with course observations or student evaluations, course syllabi offer a novel approach to predict course quality even prior to the first day of classes. This study develops an online course design characteristics rubric for science course syllabi. Utilizing content analysis, inductive coding, and deductive coding, we established four broad high-quality course design categories: course organization, course objectives and alignment, interpersonal interactions, and technology. Additionally, this study exploratively applied the rubric on 11 online course syllabi (N = 635 students) and found that these design categories explained variation in student performance.

Online courses provide flexible learning opportunities, but research suggests that students may learn less and persist at lower rates compared to face-to-face settings. However, few studies have investigated more distal effects of online education. In this study, we analyzed 6 years of institutional data for three cohorts of students in 13 large majors (N = 10,572) at a public research university to examine distal effects of online course participation. Using online course offering as an instrumental variable for online course taking, we find that online course taking of major-required courses leads to higher likelihood of successful 4-year graduation and slightly accelerated time-to-degree. These results suggest that offering online courses may help students to more efficiently graduate college.

Online learning outcomes have indicated both a gap between online and face-to-face learning and the amplification of this gap for low-income and minority learners. Evidence from studies across K-16 reveals equity issues regarding access to online courses; student attendance and achievement; and, most recently, the impact of the pandemic. This article uses Warschauer's conceptual framework of resources that shape digital inclusion-physical, human, and social-to conceptualize the equity concerns that arose during the pandemic-induced shift to emergency distance learning. This framework reveals equity issues across all three areas from abruptly moving millions into online learning environments without: requisite access to up-to-date computers and broadband internet access, the skills needed to succeed in less structured online classes, or teachers trained to effectively conduct classes online. Finally, we leverage Warschauer's framework to discuss ways to address these concerns and increase equity in online learning, as well as directions for research.

Clickstream data have been used increasingly to present students in online courses with analytics about their learning process to support self-regulation. Drawing on self-regulated learning theory and attribution theory, we hypothesize that providing students with analytics on their own effort along with the effort and performance of relevant peers will help students attribute their performance to factors under their control and thus positively influence their subsequent behavior and performance. To test the effect of the analytics and verify the proposed mechanism, we conducted an experiment in an online undergraduate course in which students were randomly assigned to receive theoretically inert questions (control condition), attribution questions (active control condition), and the analytics with attribution questions (treatment condition). The intervention significantly increased effort attribution, reduced ability attribution, and improved subsequent effort for a subgroup of students who self-reported low performance, although there was no significant impact on their performance.

This study integrates theories of achievement motivation and emotion to investigate daily academic behavior in an undergraduate online course. Using cluster analysis and hierarchical logistic regression, we analyze profiles of task values and anticipated emotions to understand expectations and completion of academic tasks over the duration of a week. Students’ task specific interest, opportunity cost, and anticipated satisfaction and regret varied across tasks and were predictive of both their expectations of task completion and actual task completion reported the following day. The results shed light on the important role of achievement motivation as situated and dynamic, highlighting the interplay between task priorities, task values, and anticipated emotions in academic task engagement. © 2021, The Author(s).

Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students' self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students' lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success-they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students' STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.

A growing body of work has shown that two specific study strategies help explain differences in learning and achievement in gateway courses: spacing (breaking up study sessions across multiple days) and self-testing (actively recalling information from memory). However, it is still unclear whether the benefits of these strategies are applicable in more advanced biology courses, and whether promoting effective study practices in these courses (spacing and self-testing) is related to increased use of these practices and greater learning outcomes. We studied two senior-level microbiology courses that were taught by the same instructor. Using a quasi-experimental design, one course additionally received a light-touch study skills intervention, where the instructor introduced the concepts of spacing and self-testing while also providing reminders to students about utilizing these strategies. We found that, while the intervention was not related to increased use of spacing and self-testing, both strategies were positively related to learning, as measured by the final course grade. Results from multiple regression analyses revealed that engaging in spacing throughout the course was the most consistent predictor of final course grade, even after accounting for other study strategies, demographic characteristics, and prior academic achievement. Our results add to the literature emphasizing the importance of spacing in increasing students’ achievement in STEM courses. © Springer Nature Switzerland AG 2021.