Browsing by Author "Hornburg, Caroline Byrd"

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    Examining the Factor Structure of the Home Mathematics Environment to Delineate Its Role in Predicting Preschool Numeracy, Mathematical Language, and Spatial Skills
    Purpura, David J.; King, Yemimah A.; Rolan, Emily; Hornburg, Caroline Byrd; Schmitt, Sara A.; Hart, Sara A.; Ganley, Colleen M. (2020-08-06)
    A growing body of evidence suggests that the ways in which parents and preschool children interact in terms of home-based mathematics activities (i.e., the home mathematics environment; HME) is related to children's mathematics development (e.g., primarily numeracy skills and spatial skills); however, this body of evidence is mixed with some research supporting the relation and others finding null effects. Importantly, few studies have explicitly examined the factor structure of the HME and contrasted multiple hypothesized models. To develop more precise models of how the HME supports children's mathematics development, the structure of the HME needs to be examined and linked to mathematics performance. The purpose of this study was to extend prior work by replicating the factor structure of the HME (as one general HME factor and three specific factors of direct numeracy, indirect numeracy, and spatial) and using those factors to predict direct assessments of children's numeracy, mathematical language, and spatial skills. It was hypothesized that the general HME factor would be related to each direct assessment, the direct numeracy factor would be related to both numeracy and mathematical language, and the spatial factor would be related to spatial skills. Using a sample of 129 preschool children (Mage = 4.71 years,SD= 0.55; 46.5% female), a series of confirmatory factor analyses were conducted. Results diverged somewhat from prior work as the best fitting model was a bifactor model with a general HME factor and two specific factors (one that combined direct and indirect numeracy activities and another of spatial activities) rather than three specific factors as had previously been found. Further, structural equation modeling analyses suggested that, in contrast to expectations, only the direct + indirect numeracy factor was a significant predictor of direct child assessments when accounting for age, sex, and parental education. These findings provide evidence that a bifactor model is important in understanding the structure of the HME, but only one specific factor is related to children's outcomes. Delineating the structure of the HME, and how specific facets of the HME relate to children's mathematics skills, provides a strong foundation for understanding and enhancing the mechanisms that support mathematics development.
  • Exploring Collaborative Patterns in Neurodiverse Teams: A Hidden Markov Model Approach Using Physiological Signals
    Kim, Sunwook; Wang, Manhua; Fok, Megan; Hornburg, Caroline Byrd; Jeon, Myounghoon; Scarpa, Angela (SAGE Publications, 2024-09)
    Autistic individuals face challenges in successful employment, emphasizing the need for targeted workplace support. This study explored collaborative dynamics within neurodiverse teams during a simulated remote work task by applying Hidden Markov Models (HMMs) to heart rate data. Eighteen participants formed nine dyads: six nonautistic (NA-NA) pairs and three autistic-non-autistic (ASD-NA) pairs. Dyads completed two trials of a collaborative programming task over Zoom, alternating roles between trials. Heart rate data were collected, segmented, and transformed to extract features reflecting participants’ interactions. The final HMM was fitted with seven hidden states, and transition probabilities were derived for each dyad type. Results showed that NA-NA dyads exhibited more frequent transitions among states compared to ASD-NA dyads, potentially suggesting more varied interaction patterns. These findings demonstrate the utility of HMMs in capturing collaborative behaviors through physiological signals and highlight their potential in helping develop effective support strategies for neurodiverse teams.
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    Perceptual and Number Effects on Students’ Initial Solution Strategies in an Interactive Online Mathematics Game
    Lee, Ji-Eun; Hornburg, Caroline Byrd; Chan, Jenny Yun-Chen; Ottmar, Erin (PsychOpen, 2022-03-31)
    This study investigated the effects of 1) proximal grouping of numbers, 2) problem-solving goals to make 100, and 3) prior knowledge on students’ initial solution strategies in an interactive online mathematics game. In this game, students transformed an initial expression into a perceptually different but mathematically equivalent goal state. We recorded students’ solution strategies and focused on the productivity of their first steps—whether their initial action led them closer to the goal. We analyzed log data within the game from 227 middle-school students solving four addition problems and four multiplication problems consisting of a total of 1,816 problem-level data points. Logistic regression modeling showed that students were more likely to use productive initial solution strategies to solve addition and multiplication problems when 1) proximity supported number grouping, 2) 100 was the problem-solving goal, and 3) students had higher prior knowledge in mathematics. Furthermore, when problem-solving goals were non-100s, students with lower prior knowledge were less likely to use productive initial solution strategies than students with higher prior knowledge. The findings of the study demonstrated that perceptual and number features influenced students’ initial solution strategies, and the effect of number features on initial solution strategies varied by students’ prior knowledge. Results yield important implications for designing instructional activities that support mathematics learning and problem-solving.
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    Preschoolers' Mathematical Language Learning during Book Reading with an AI Voice Agent
    Kim, Jisun (Virginia Tech, 2024-08-06)
    Digital media technologies have been extensively utilized in children's daily lives and many researchers, educators, caregivers, and developers have been interested in finding ways to utilize these technologies in educational settings to facilitate early cognitive development. Among a wide range of media technologies, the accessibility of voice assistants and smart speakers powered by Artificial Intelligence (AI) has notably increased. However, there is a paucity of knowledge about how this advanced technology can be used to teach young children important mathematical concepts during shared book reading. The current study aimed to examine whether and under what circumstances shared book reading with an AI voice agent would enhance preschool-aged children's learning of mathematical language, a critical domain-specific language highly associated with early numeracy skills and vocabulary development. Sixty-six participants who were recruited for home-visit and school-visit sessions were randomly assigned to one of three reading conditions to read a storybook with the AI voice agent three times: math storybook reading with dialogic questions, math storybook reading without dialogic questions, and non-math storybook reading with dialogic questions. The findings indicate that shared math storybook reading supports children's target mathematical language learning differently based on their initial understanding of numeracy skills. Children with higher levels of numeracy skills demonstrated greater benefits from simply listening to the story, whereas children with lower levels of numeracy skills showed a tendency to learn better when hearing questions and feedback from the AI voice agent. This study provides implications for the use of advanced technology involving social interaction to support children's learning of key mathematical language that can benefit from repeated reading.