Self-regulated learning (SRL) is a cognitive and metacognitive process through which students develop the self-awareness necessary to direct their learning based on their needs to reach a desired outcome. Despite 40 years of literature, SRL has no singular definition, as it is often used in domain-specific research that is not always transferable to other fields. Regardless, much of the literature speaks to the importance of SRL regarding academic success. This paper details the development of an SRL instrument designed to identify key self-regulatory constructs in an undergraduate introductory physics classroom. Confirmatory factor analysis supported a four-factor model measuring Planning, Time & Environment Management, Comprehension Monitoring & Evaluation, and Peer Learning & Help-Seeking as unique facets of self-regulated learning. While most behaviors did not significantly evolve over one semester, students reported significantly lower scores on the Comprehension Monitoring & Evaluation factor between the beginning and end of the semester. Higher performing students, as measured by their average homework grades, scored significantly higher on the Time & Environment Management factor and the Peer Learning & Help-Seeking factor at both time points. Additionally, SRL behaviors were significantly predicted by personality facets from the Big Five Inventory, with Conscientiousness, Extraversion, and Openness being the most related to certain behaviors.

Time management is crucial for college students' academic success and learning of computer programming. Yet the changes of time management behaviors and their associations with learning outcomes are underexplored in online learning of programming. To address the gap, this study employed an intensive longitudinal approach to examine undergraduates' time management behaviors in an online programming problem system. Specifically, we analyzed weekly indicators of academic procrastination and spaced practice derived from programming traces. We applied dynamic structural equation modeling to examine the changes in these behaviors over time and their correlations with weekly quiz performance. Academic procrastination and selfselected spaced practice showed a significant upward trend over time, while incentivized spaced practice exhibited a significant downward trend. Moreover, students with prior programming experience showed a greater growth rate in spacing behaviors. At both within- and between -person levels, procrastination predicted quiz performance significantly and negatively, while self-selected spaced practice predicted quiz performance significantly and positively. In contrast, incentivized spaced practice predicted quiz performance positively at the within -person level but negatively at the between -person level. Additionally, quiz performance in the current week predicted subsequent time management behaviors significantly. These findings contribute to the understanding of procrastination and spaced practice in online programming learning and have implications for the design of scaffolding on time management. Furthermore, this study demonstrates the significance of combining intensive longitudinal approaches and action logs in examining the temporality of learning in online environments.

Increasing evidence suggests that success in science, technology, engineering, and math (STEM) fields is not only dependent upon one's actual STEM-relevant abilities but also upon one's STEM-relevant attitudes-in particular, math and spatial attitudes. Here, we examine whether simply mentioning the math or spatial relevance of a task affects children's performance and the moderating role of children's math and spatial attitudes. Further, we examine gender differences in performance given pervasive gender gaps in STEM and early-emerging gender differences in math and spatial attitudes. Participants (221 first- to fourth-grade children from the United States; 113 girls, 108 boys; 52% White, 16% Black, 14% Asian, 9% Hispanic or Latinx, 18% multiple races/ethnicities) were introduced to a novel task framed as tapping into math or spatial abilities (or no framing [control condition]). Children then completed math and spatial anxiety and self-concept measures. Results indicate that children who heard the math task framing were less accurate relative to children in the control condition, and the effect was larger for those with higher math anxiety or lower math self-concept, but it was not different for boys and girls. Children who heard the spatial task framing, however, performed comparably to children in the control condition. Though both math and spatial attitudes revealed identical patterns of gender differences (with higher anxiety and lower self-concept in girls than boys), there were no gender differences in performance. This study highlights the salient role of math attitudes early in development and provides key insights for future work aimed at increasing STEM outcomes.

The construction industry has been a predominantly White/Caucasian Men community with a very low representation of women and people from traditionally marginalized backgrounds. Even though companies have been implementing Diversity, Equity, and Inclusion (DEI) statements for many years, we still believe it is neither a diverse nor equitable field. To better understand how DEI statements declared by companies have been understood and recognized by employees, a survey was deployed nationwide to understand how professionals in the construction industry perceive their organization's DEI statements or policies. A complete data set was built from 249 participants. 75% identified themselves as men and 25% as women, and nobody identified with other gender identities. More than 80% of participants were White/Caucasian, 4% Black or African American, 4% Hispanic or Latinx, and 6% Asian. Participants are currently working in small (24%), medium (30%), and large (46%) construction and design companies located across The United States. Regarding the number of employees, companies are small, less than 99 employees; medium, between 100 and 499 employees; and large, more than 500 employees. Also, companies were grouped into four main types, building construction companies (67%), transportation construction companies (6%), special trade contractor companies (17%), and design companies (10%). For more than 65% of professionals in the construction industry who participated in this study, DEI was mainly related to proper representation of women and minoritized populations in the workforce; Merit-based transparent recruitment and promotion; equality, social justice, and nondiscrimination policy statement; and equitable payment and compensation. Other factors such as proper representation of women and minoritized populations at the top management level and payment structure transparency did not emerge from the results. We also found that 70% of professionals identified DEI statements in their companies and 30% of professionals did not identify or did not know about DEI statements. Looking at the company size, 85% of professionals in large companies identified DEI statements in their companies, but 71% and 42% of professionals in medium and small companies identified DEI statements in their companies, respectively. According to the company type, more than 80% of professionals working in design companies recognized DEI statements in their companies, but around 60% in construction and special trade companies. We can highlight that large companies have established policies and practices that result in better socialization and recognition of their DEI statements than medium and small companies. Also, construction and special trade companies need to strengthen their DEI statements and increase the representation of women and people from traditionally marginalized backgrounds. Results from this research give an idea about the current state of DEI in the construction industry and would contribute to the current effort to increase the diversity of the nation's construction workforce.

How teachers attend to and interpret positive relational interactions shapes how they enact instructional practices for equity. We draw on frameworks from equitable mathematics instruction, relational interactions, and teacher noticing to conceptualize mathematics teachers' relational noticing. Using noticing interview and classroom observation data from a research collaborative between secondary mathematics teachers and university-based teacher educators, we document the range and diversity of ten teachers' relational noticing. We use this analysis to examine how teachers' relational noticing supports enacting equitable instructional practices. Our findings indicate five themes of teachers' relational noticing that are informed by their personal histories, understanding of dominant narratives of mathematics education, and their local sociopolitical school context. Additionally, teachers enacted a range of practices for creating positive relational interactions, with attending to student thinking being the most enacted practice. Our findings suggest that mathematics teachers' relational noticing can support the three axes of equitable instruction.

On average, women faculty take on more childcare responsibilities, posing barriers to career success. Work-family policies represent one solution for advancing gender equity in academia as they support parents after childbirth with benefits for children, employees, and organizations. We contribute to understanding how the availability and use of dependent care policies (paid parental leave and childcare benefits) relate to long-term research productivity trends. Based on the work-home resources model, we theorize that policy availability provides contextual resources and policy use provides personal resources, leading to improvements in an individual's research productivity after they have a child. We also examine potential gender differences in the effect of dependent care policies on research productivity trends. We leverage n = 6945 yearly top publications and h-index observations for 386 business professors from 108 universities. Consistent with our hypotheses, the availability and use of paid parental leave and childcare benefits were associated with increased research productivity trends, though the effects depend on birth order, policy, and gender to some extent. Our findings have considerable theoretical and practical implications for organizations and society.

The growing use of generative AI tools built on large language models (LLMs) calls the sustainability of traditional assessment practices into question. Tools like OpenAI's ChatGPT can generate eloquent essays on any topic and in any language, write code in various programming languages, and ace most standardized tests, all within seconds. We conducted an international survey of educators and students in higher education to understand and compare their perspectives on the impact of generative AI across various assessment scenarios, building on an established framework for examining the quality of online assessments along six dimensions. Across three universities, 680 students and 87 educators, who moderately use generative AI, consider essay and coding assessments to be most impacted. Educators strongly prefer assessments that are adapted to assume the use of AI and encourage critical thinking, while students' reactions are mixed, in part due to concerns about a loss of creativity. The findings show the importance of engaging educators and students in assessment reform efforts to focus on the process of learning over its outputs, alongside higher-order thinking and authentic applications. © 2024 The Author(s)

Prior research suggests most students do not glean valid cues from provided visuals, resulting in reduced metacomprehension accuracy. Across 4 experiments, we explored how the presence of instructional visuals affects students' metacomprehension accuracy and cue-use for different types of metacognitive judgments. Undergraduates read texts on biology (Study 1a and b) or chemistry (Study 2 and 3) topics, made various judgments (test, explain, and draw) for each text, and completed comprehension tests. Students were randomly assigned to receive only texts (text-only condition) or texts with instructional visualizations (text-and-image condition). In Studies 1b, 2 and 3, students also reported the cues they used to make each judgment. Across the set of studies, instructional visualizations harmed relative metacomprehension accuracy. In Studies 1a and 2, this was especially the case when students were asked to judge how well they felt they could draw the processes described in the text. But in Study 3, this was especially the case when students were asked to judge how well they would do on a set of comprehension tests. In Studies 2 and 3, students who reported basing their judgments on representation-based cues demonstrated more accurate relative accuracy than students who reported using heuristic based cues. Further, across these studies, students reported using visual cues to make their draw judgments, but not their test or explain judgments. Taken together, these results indicate that instructional visualizations can hinder metacognitive judgment accuracy, particularly by influencing the types of cues students use to make judgments of their ability to draw key concepts.

Metacognitive calibration-the capacity to accurately self-assess one's performance-forms the basis for error detection and self-monitoring and is a potential catalyst for conceptual change. Limited brain imaging research on authentic learning tasks implicates the lateral prefrontal and anterior cingulate brain regions in expert scientific reasoning. This study aimed to determine how variation in undergraduate life sciences students' metacognitive calibration relates to their brain activity when evaluating the accuracy of biological models. Fifty undergraduate students enrolled in an introductory life sciences course completed a biology model error detection task during fMRI. Students with higher metacognitive calibration recruited lateral prefrontal regions linked in prior research to expert STEM reasoning to a greater extent than those with lower metacognitive calibration. Findings suggest that metacognition relates to important individual differences in undergraduate students' use of neural resources during an authentic educational task and underscore the importance of fostering metacognitive calibration in the classroom.

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. © 2024 The Authors