Using nationally representative data from 2000 to 2021, we examine the distribution of teacher characteristics and qualifications over time, with an emphasis on early career teacher (ECT) qualifications, access to induction supports, and perceptions of preparedness and their distribution across majority-minority, high-poverty, and rural schools. We find the proportion of ECTs and first-year teachers has remained mostly stable and even declined slightly between 2000 and 2021. Despite efforts to diversify the profession in recent years, there is little change in the proportion of teachers of color. ECTs have greater access to induction supports and feelings of preparedness on most measures over time, particularly in nonrural schools. However, we find growing disparities between teacher qualifications and preparedness in rural and nonrural school contexts.

Qualitative video analysis is a foundational yet labor-intensive method in the learning sciences, particularly for investigating the multimodal and collaborative dimensions of learning. This paper introduces GAIVA (Generative Artificial Intelligence for Video Analysis), a novel framework that integrates multimodal large language models (MLLMs) and large language models (LLMs) to support scalable, consistent, and theory-informed video analysis. GAIVA comprises four sequential phases: (1) video preprocessing, (2) MLLM-based behavioral transcription, (3) human-AI collaborative codebook development grounded in theoretical constructs from computer-supported collaborative learning (CSCL), and (4) LLM-based automated coding. Drawing on a case study to analyze simulation-based collaborative science learning, we demonstrate that GAIVA generates rich behavioral transcriptions, enables the construction of stable and theoretically grounded coding schemes, and achieves inter-rater reliability comparable to human annotation. Notably, findings illustrate GAIVA's capacity to support both deductive alignment and inductive discovery. The framework surfaces a behavioral construct--Peripheral Engagement--that is consistent across data batches but absent from existing frameworks. We contribute a systematic and replicable framework for integrating generative AI into qualitative video research, enabling large-scale, multimodal analysis of collaborative learning while preserving analytical rigor, theoretical coherence, and interpretive transparency.

In order to rectify the historical marginalization of Black workers in computing fields, many academic and industrial initiatives have been established to increase the diversity and inclusion in the tech industry. In this study, we explored how one particular post-secondary education program at one historically black institution known as the Google in Residence (GIR) program impacted students' interests and their pursuit of computing-related careers. Data on employment outcomes at this institution showed that the GIR participants who obtained internships and full-time employment at Google were predominantly international students, not African American students who make up the majority of the undergraduate student and about half of the GIR participants. To understand this phenomenon better, we interviewed 27 students who spanned all 4 years of study and included African American and international students. The interviews were structured to elicit a wide range of factors influencing career decisions. Our analysis describes the impact of a particular GIR introductory-level programming course and other GIR program impacts beyond the introductory-level course. We found that the GIR introductory course was engaging, inspired students to change their degree in a more computing-oriented direction and supported internship applications. The informational workshops, networking events, mock-interviews, and advising of the computer science club, which were supported by the GIR program, built students' confidence in interviewing and obtaining full-time employment within the tech industry. While the GIR course and program had a positive influence on students of all backgrounds, the reputation of Google was especially motivating for international students. However, the African American students in this study generally had career aspirations and goals that were less tied to specific companies or industries. Based on our findings, potential improvements could include broadening awareness and access of the GIR course to non-computer science majors, offering summer programs to increase students' computational confidence, and increasing the diversity of GIR and computer science instructors.

There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of affect, distinct from other prominent affective categories. We discuss how these types of affect have been considered historically and in key models. We then discuss unusual manifestations of each form of affect that have been documented in the literature, and what light they shed on the broader constructs. We conclude with a discussion of a new theoretical framing that treats confusion and frustration as a confrustion constellation, and the opportunities and open questions that this perspective presents.

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.