The term disability encompasses many conditions (including a range of learning, intellectual, physical, sensory and socioemotional disorders) that can be caused by a variety of genetic, environmental, and unknown factors. We examine how children reason about the biological nature of disabilities, specifically the extent to which they use 'essentialist', 'infectious disease', or 'bodily damage' causal models. These models provide competing predictions regarding the biological nature of disability. The essentialist model views disabilities as caused by an internal essence, akin to genes, and entails thinking of disabilities as stable, immutable, and inheritable. The infectious disease model views disabilities as communicable, abnormal, and needing intervention. The bodily damage model views disabilities as resulting from injuries or toxins, which maybe stable but are not inheritable or transmissible. We review what is known about children's acquisition of these models, and discuss how disentangling these biological models is a fruitful avenue for future research.

The purpose of this systematic review was to identify how the home learning environment (HLE) was measured in group design, early math intervention studies conducted in the home. Specifically, we evaluated the physical (e.g. frequency of activities) and affective (e.g. parents' beliefs, children's attitudes) aspects of the HLE. We included intervention studies conducted with parents and young children (ages 3-9 years old) that also included instruments designed to measure the HLE. We included 16 studies that used 21 HLE instruments (16 surveys, 3 interviews, 2 focus groups); we coded the characteristics of the HLE instruments, including which physical and affective aspects of the HLE the instruments measured. In most cases, parents responded to the instruments; whereas, only two studies used instruments that captured children's perspectives of the HLE. The instruments measured physical aspects more often than affective aspects of the HLE. Findings from this systematic review highlight implications for measuring the HLE and intervention development and implementation.

Underrepresented minority (URM) faculty face challenges in many domains of academia, from university admissions to grant applications. We examine whether this translates to promotion and tenure (P&T) decisions. Data from five US universities on 1,571 faculty members' P&T decisions show that URM faculty received 7% more negative votes and were 44% less likely to receive unanimous votes from P&T committees. A double standard in how scholarly productivity is rewarded is also observed, with below-average h-indexes being judged more harshly for URM faculty than for non-URM faculty. This relationship is amplified for faculty with intersectional backgrounds, especially URM women. The differential treatment of URM women was mitigated when external reviewers highlighted candidates' scholarship more in their review letters. In sum, the results support the double standard hypothesis and provide evidence that different outcomes in P&T decision-making processes contribute to the sustained underrepresentation of URM faculty in tenured faculty positions. Masters-Waage et al. report that underrepresented minority (URM) faculty in the USA face barriers in the promotion and tenure process, receiving more negative votes and fewer unanimous positive decisions at the college level. This is partly due to a double standard: URM faculty are held to a higher standard than non-URM faculty in terms of scholarly productivity.

Purpose: Children with developmental language disorder frequently have difficulty with both academic success and language learning and use. This clinical focus article describes core principles derived from a larger program of research (National Science Foundation 1748298) on language intervention combined with science instruction for preschoolers. It serves as an illustration of a model for integrating language intervention with curricular content delivery. Method: We present a five-step model for a speech-language pathologist and other school professionals to follow to (a) understand the grade-level core curriculum objectives; (b) align intervention targets with the curriculum; (c) select a therapy approach that aligns with both goals and curricular content, and (d) methods for implementing the intervention; and (e) verify that both the intervention and the curriculum have been provided in accordance with best practices. We apply this model to the Next Generation Science Standards, a science curriculum popular in the United States, and to grammar and vocabulary interventions, two areas of difficulty for children with developmental language disorders, though it would be possible to extend the steps to other curricular areas and intervention targets. Conclusions: We conclude by discussing the barriers and benefits to adopting this model. We recognize that both speech-language pathologists and teachers may have limited time to implement language intervention within a general education curriculum, but we suggest that the long-term benefits outweigh the barriers. © 2024 American Speech-Language-Hearing Association

The work of elementary science teaching is challenging given the wide array of subject matter most teachers are expected to teach and a systematic de-prioritisation of science at these grades. In this literature review (63 papers; 2010-2020), we use a framework of readiness for science teaching. Using this framework allows us to illustrate foundational characteristics and abilities that preservice teachers may start with and develop as they become well-started beginners for elementary teaching in the face of systemic challenges. To this end, we identify what is known from the research literature about the strengths that preservice elementary teachers bring to this difficult work with regard to their characteristics and abilities in addition to the challenges they face, describing a foundation on which preservice teachers can build. We also highlight additional studies that show how teacher education can build on preservice teachers' strengths and support them in areas that are challenging. We identify themes around novices' identities, dispositions, emotions, beliefs, attitudes, self-efficacy, knowledge, engagement in and with science practices, lesson planning, and lesson enactment. Finally, we highlight four implications for science teacher educators, noting focal areas that may compensate for challenges preservice elementary teachers face while building on their strengths.

The onset of the COVID-19 pandemic and associated long-term shifts to virtual instruction among most US schools presented notable challenges among education researchers. Ongoing projects conducted in school settings experienced sudden losses of access to teacher and student participants, in many cases leading to severe interruptions to data collection efforts. Perhaps most notably, upon returns to in-person instruction in the 2021/22 academic year most schools instigated strict policies limiting the number of non-school personnel who could enter school buildings, including researchers conducting in-person data collections. As such, many researchers had to find alternative means to gather data. In this paper, we offer a new protocol that we created in response to these challenges that allows for the secure and fully remote collection of video data in school settings. This new protocol not only addressed the immediate needs of the focal study but also addresses some of the most notable barriers to collecting classroom video data in the field of education research at large. In this paper, we describe the initial development and application of this protocol among a local study of elementary teachers, as well as the scaling of this protocol in a study of elementary teachers in multiple states. It is our hope that this protocol can expand education researchers’, practitioners’, and policymakers’ access to classroom video data. © The Author(s) 2024.

Student’s shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to assess the attentional state continuously and non-intrusively over time and hence enable large-scale research on learning materials and responding to inattention with targeted interventions. To achieve this, an accessible detection approach that performs well for various systems and settings is required. In this work, we explore a new, generalizable approach to video-based mind wandering detection that can be transferred to naturalistic settings across learning tasks. Therefore, we leverage two datasets, consisting of facial videos during reading in the lab (N = 135) and lecture viewing in-the-wild (N = 15). When predicting mind wandering, deep neural networks (DNN) and long short-term memory networks (LSTMs) achieve F1 scores of 0.44 (AUC-PR = 0.40) and 0.459 (AUC-PR = 0.39), above chance level, with latent features based on transfer-learning on the lab data. When exploring generalizability by training on the lab dataset and predicting on the in-the-wild dataset, BiLSTMs on latent features perform comparably to the state-of-the-art with an F1 score of 0.352 (AUC-PR = 0.26). Moreover, we investigate the fairness of predictive models across gender and show based on post-hoc explainability methods that employed latent features mainly encode information on eye and mouth areas. We discuss the benefits of generalizability and possible applications. © The Author(s) 2024.

Undergraduates enrolled in large, active learning courses must self-regulate their learning (self-regulated learning [SRL]) by appraising tasks, making plans, setting goals, and enacting and monitoring strategies. SRL researchers have relied on self-report and learner-mediated methods during academic tasks studied in laboratories and now collect digital event data when learners engage with technology-based tools in classrooms. Inferring SRL processes from digital events and testing their validity is challenging. We aligned digital and verbal SRL event data to validate digital events as traces of SRL and used them to predict achievement in lab and course settings. In Study 1, we sampled a learning task from a biology course into a laboratory setting. Enrolled students (N = 48) completed the lesson using digital resources (e.g., online textbook, course site) while thinking aloud weeks before it was taught in class. Analyses confirmed that 10 digital events reliably co-occurred >= 70% of the time with verbalized task definition and strategy use macroprocesses. Some digital events co-occurred with multiple verbalized SRL macroprocesses. Variance in occurrence of validated digital events was limited in lab sessions, and they explained statistically nonsignificant variance in learners' performance on lesson quizzes. In Study 2, lesson-specific digital event data from learners (N = 307) enrolled in the course (but not in Study 1) predicted performance on lesson-specific exam items, final exams, and course grades. Validated digital events also predicted final exam and course grades in the next semester (N = 432). Digital events can be validated to reflect SRL processes and scaled to explain achievement in naturalistic undergraduate education settings.

Gender disparities persist in postsecondary computing fields, despite improvements in postsecondary equity overall and STEM fields as an aggregate. The entrenchment of this issue requires a comprehensive, longitudinal lens. Building on expectancy-value theory, the present study examines the relationships among students’ gender-ability stereotypes, attainment values, course-taking, and major choices. Using data from the High School Longitudinal Study of 2009 (HSLS: 2009), we applied weighted t-tests and multiple-group structural equation modeling to investigate how motivational beliefs (i.e., gender-ability stereotypes, attainment values) and course-taking patterns in math and science may predict major choice in computing. Overall, we find gender differences in identity-based mathematics and science motivational beliefs have long-term effects. Gender-ability stereotypes in math and science shape attainment values in each domain, whereby stereotypes suppress girls’ attainment values and enhance boys? attainment values (p < 0.001), in turn shaping course-taking and major decisions. Math- and sciencerelated motivational and curricular factors affect “other” STEM more than computing major outcomes. Specifically, computer science course-taking is completed more by boys (d = 0.21), but girls’ chances of declaring computing majors are especially enhanced by completing these courses in high school. Advanced science course-taking and science attainment value positively predict boys’ but not girls’ likelihood of declaring computing majors. We discuss the implications of these findings for research, policy, and practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.

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.