Completing advanced high school math coursework relates to better adulthood outcomes. Our understanding of why youth with learning disabilities (LDs) and/or ADHD have less access to high math course attainment is limited. Using data on around 20,000 adolescents from the High School Longitudinal Study of 2009, results indicate that, regardless of disability status, structural inequities in family social position are more salient for youth's math course attainment than formal disability programming, universal supports, or structural inequities in how students are sorted across schools. Among youth with the same disability status, youth from higher SES families, or whose parents have a STEM degree, have heightened access to high math course attainment even after accounting for prior achievement. Disparities in access to high math course attainment that persist net of controls for both youth with an LD and youth with ADHD present the possibility of disability-related stratification and stigma during high school.

The importance of early numerical and executive function (EF) skills is well-established, with each skill set positively and specifically predicting later mathematics achievement, income, postsecondary education, and more. Less is known, however, about the relations between EF and numerical skills. Therefore, we examined the concurrent and predictive relations between EF and numerical skills in preschoolers to third graders (N = 205; 4.67-8.75 years of age; 43.9% female; 51.2% White non-Hispanic, 18% multiracial, 6.3% Hispanic, 12.2% Black, 2% American Indian/Alaska Native, 4.9% Asian, 1% not otherwise listed). We found positive concurrent relations between EF and all six numerical skills examined: nonsymbolic magnitude comparison, verbal counting, numerical literacy, count on, non-rote counting, and numerical problem solving. There were unidirectional predictive relations between EF and four of the six numerical skills after controlling for covariates and prior performance on the skill of interest. Bidirectional relations were found only for EF and nonsymbolic magnitude comparison. We also found that the concurrent relation between EF and count on was higher for children with typical versus persistently low mathematics achievement. All other concurrent and predictive relations were similar for children with typical and persistently low mathematics achievement. Overall, these findings show that the relations between EF and numerical skills are both pervasive and nuanced, such that they vary by timing of assessments (i.e., concurrent or predictive) and numerical skill. These results can inform future theoretical models on the role of EF in numerical development , have practical implications for designing interventions targeting these skill sets in early childhood. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training , similar technologies.

To determine their academic strengths and weaknesses, students compare their own performance across domains (e.g., math vs. English), a process referred to as dimensional comparisons. For example, individuals' higher-scoring English performance may negatively affect their math motivational beliefs (competence self-concepts and intrinsic values), resulting in favoritism toward English. Students' motivation can also be affected by praise from adults. However, praise in one domain (e.g., English) may have unexpected negative effects on motivation in the contrasting domain (e.g., math) through dimensional comparisons. We experimentally investigated the impact of receiving praise in only one domain on students' domain-specific motivational beliefs. We hypothesized that students would have higher motivational beliefs in the praised domain and lower motivational beliefs in the nonpraised domain compared with students who received no praise. Seventh- to ninth-graders (10- to 15-year-olds; N = 108; 46 girls; 92 living in the United States; 84.8% White, 2.9% Asian or Asian American, 2.9% Black or African American, 9.5% multiple races; parents' education range: 13-18 years) showed heightened verbal competence self-concepts after receiving praise on either verbal or math performance. College students (first to fifth year; N = 109; 89 women; 105 living in the United States; 58.9% White, 21.5% Asian or Asian American, 10.3% Black or African American, 5.6% multiple races, 3.7% other races) showed higher verbal intrinsic values after receiving praise on verbal performance. Results supported positive effects of praise in the verbal domain only and were inconsistent with the predicted negative effects on the non-praised domain. We suggest that students' verbal motivational beliefs are more malleable than math beliefs when receiving disproportionate praise. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Interactive shared book reading – particularly when parents engage in decontextualized talk which goes beyond the here-and-now – is an evidenced-based approach to promote preschool-aged children's engagement with a story and emergent literacy. Parental training of interactive reading often involves an experienced clinician or researcher who can provide personalized, “in-the-moment” instruction. Although such methods are highly effective, they are expensive and time-intensive, often limiting participation by busy parents. Here, we extend this approach through a digital tool designed to encourage dyadic interaction during shared book reading via a conversational agent (CA). In this paper, we describe the results of a one-month usability test of the app with N = 20 parents and their 3-to-5-year-old children. The app first provided parents with information on interactive reading strategies. During reading, it used AI technology to deliver interactive prompts aimed at promoting decontextualized conversation. Parent-child conversation while reading with the CA was nearly double that of conversation when reading without the CA. Approximately one month later, parent and child talk while reading with CA remained high. Additional analyses indicated that parents most often expanded on the CA's prompts rather than repeating them, thereby eliciting more verbal responses from children. Results indicate that CAs have the potential to enrich dyadic interactions around print books in ways known to promote children's early literacy abilities. Future directions around the scalability and efficacy of CA technologies to augment literacy practices are discussed. © 2024 Elsevier B.V.

Scientific reasoning is a critical foundational skill learners need to practice and know for increased science learning outcomes. Game-based learning environments (GBLEs) provide learners a platform for developing and practicing scientific reasoning skills but little is known about how learners should engage in scientific reasoning during game-based learning. As such, this paper aimed to understand if and how learners engaged in effective scientific reasoning activities during learning with a GBLE. This paper used analytical techniques from Complex Systems Theory to quantify learners’ scientific reasoning actions during game-based learning. High-school students (N = 170) played Crystal Island, a microbiology GBLE requiring learners to engage in scientific reasoning to successfully identify an illness infecting residents of a virtual island. Categorical auto-Recurrence Quantification Analysis was run on participants’ log files as they deployed scientific reasoning actions. This analysis revealed several metrics of complexity, including recurrence rate which is the proportion of repetitive to novel scientific reasoning actions. Results found that as time progressed, recurrence rates decreased. Successful learners (i.e., those who solved the mystery) demonstrated less repetition in their scientific reasoning activities where their recurrence rates decreased at a slower rate over time than learners who were unsuccessful in solving the mystery. Findings provide implications for adaptively scaffolding learners’ emerging complexities in their scientific reasoning processes during game-based learning to increase learners’ GBLE success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users' moment-by-moment attention. Our study focuses on the incremental prediction of attention as a person is seeing an image and hearing a referring expression defining the object in the scene that should be fixated by gaze. To predict the gaze scanpaths in this incremental object referral task, we developed the Attention in Referral Transformer model or ART, which predicts the human fixations spurred by each word in a referring expression. ART uses a multimodal transformer encoder to jointly learn gaze behavior and its underlying grounding tasks, and an autoregressive transformer decoder to predict, for each word, a variable number of fixations based on fixation history. To train ART, we created RefCOCO-Gaze, a large-scale dataset of 19,738 human gaze scanpaths, corresponding to 2,094 unique image-expression pairs, from 220 participants performing our referral task. In our quantitative and qualitative analyses, ART not only outperforms existing methods in scanpath prediction, but also appears to capture several human attention patterns, such as waiting, scanning, and verification. Code and dataset are available at: https://github.com/cvlab-stonybrook/ART.

Training gaze following models requires a large number of images with gaze target coordinates annotated by human annotators, which is a laborious and inherently ambiguous process. We propose the first semi-supervised method for gaze following by introducing two novel priors to the task. We obtain the first prior using a large pretrained Visual Question Answering (VQA) model, where we compute Grad-CAM heatmaps by 'prompting' the VQA model with a gaze following question. These heatmaps can be noisy and not suited for use in training. The need to refine these noisy annotations leads us to incorporate a second prior. We utilize a diffusion model trained on limited human annotations and modify the reverse sampling process to refine the Grad-CAM heatmaps. By tuning the diffusion process we achieve a trade-off between the human annotation prior and the VQA heatmap prior, which retains the useful VQA prior information while exhibiting similar properties to the training data distribution. Our method outperforms simple pseudo-annotation generation baselines on the GazeFollow image dataset. More importantly, our pseudo-annotation strategy, applied to a widely used supervised gaze following model (VAT), reduces the annotation need by 50%. Our method also performs the best on the VideoAttentionTarget dataset. Code is available at https://github.com/cvlab- stonybrook/GCDR-Gaze.git.

IntroductionIndividuals' math value beliefs are theorized to influence who persists in STEM. However, the existing findings on gender differences in adolescents' math value beliefs are inconsistent. The goal of this study was to use three existing datasets to help clarify when gender differences emerge for high school adolescents and for whom (i.e., adolescents across historical time, grade level, and race/ethnicity). Specifically, we examined the extent to which gender differences in adolescents' math value beliefs (i.e., interest, utility, and attainment) replicated (1) across three datasets spanning the 1990s to 2010s, (2) from 9th-12th grade, and (3) within each of the four largest U.S. racial/ethnic groups (i.e., Asian, Black, Latine, and White adolescents).MethodsWe tested these aims with three existing longitudinal U.S. datasets: the California Achievement Motivation Project (CAMP) (n = 8855), the Childhood and Beyond Study (CAB) (n = 582), and the High School Longitudinal Study (HSLS) (n = 21,000). Students were in high school (9th-12th grade) and half were girls (49%-53%). All three datasets included measures with the same or similar math value belief items, making conceptual replication possible.Results and ConclusionsOverall, we did not find strong evidence for meaningful gender differences in adolescents' math value beliefs overall. We did find meaningful gender differences in the oldest data set (CAB). When examined within each racial/ethnic group, we found no evidence of gender differences in math value beliefs among Black or Latine adolescents, but some differences among Asian and White adolescents. The findings align with the gender similarities hypothesis, suggesting adolescent girls and boys had similar math value beliefs.

The present research applied a multidimensional framework to the study of gender stereotypes by investigating whether elementary school children display different levels of endorsement when considering distinct gender stereotype constructs (ability, category, and interest) and feminine versus masculine stereotypes. Study 1 (N = 403) compared children's ability and category beliefs using a set of gender-neutral skill items. Study 2 (N = 539) extended this research by examining whether children showed different patterns of ability and category decisions for feminine versus masculine occupational items. Study 3 (N = 974) furthered our understanding of the construct dimension by comparing children's interest and ability decisions within the STEM domain. Findings revealed that older elementary school children endorsed ability stereotypes more strongly than category stereotypes and, across all age groups, children endorsed interest stereotypes more strongly than ability stereotypes. Findings also revealed age differences in how children think about masculine versus feminine stereotypes. For masculine stereotypes, younger children showed stronger endorsement than older children; however, for feminine stereotypes, the reverse pattern was found such that older children showed more stereotyped thinking than younger children. The present study illustrates the benefits of employing a multidimensional framework to gain a more nuanced understanding of how children apply their increasing knowledge of gender stereotypes.

Students who exhibit mature number sense make sense of numbers and operations, use reasoning to notice patterns, and flexibly choose effective problem-solving strategies (McIntosh et al., 1997, https://ro.ecu.edu.au/ecuworks/6819). Due to its dispositional nature, mature number sense is typically measured through in-depth interviews or tests of strategy usage. Yet, the lack of an efficient, rigorously developed measure has made it difficult to collect systematic, replicable evidence on students' mature number sense. To address this, we developed a brief assessment of mature number sense. The present study provides additional convergent evidence of validity for this measure with US students in grades 3-8 (8-14 years old). We compared middle school (N = 40) and upper elementary school (N = 41) scores from the brief assessment with an established, time-intensive measure (Yang, 2019, https://doi.org/10.1007/ s10649-018-9874-8) and an in-depth interview of student strategy usage (Markovits & Sowder, 1994, https://doi.org/10.2307/749290). We found strong correlations (r > 0.7) across all three measures, and this held even when controlling for students' arithmetic scores (pr > 0.6). Researchers and educators can now use the brief assessment to investigate students' mathematical thinking and advance knowledge of a key aspect of mathematical cognition.