16 research outputs found

    Apoorva Shivaram's Quick Files

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    The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

    Continuity of same/different relational learning in toddlerhood

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    Modeling Student Math Achievement Across Countries with Machine Learning Using TIMSS 2019

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    Children’s early math skills are critical for future academic success. To profile the most important predictors of student math achievement, this study applies empirically driven supervised machine learning (ML) techniques to the Trends in Mathematics and Science Study (TIMSS) dataset that is a large-scale, international, nested, secondary dataset. This study seeks to determine what model class (random forest, gradient-boosted trees, multivariate adaptive regression splines, or stacked generalization) is best at reducing model error in predicting student math achievement and how these models differ across 39 countries. By using cross-validated iterative ML techniques, it also aims to establish the student, teacher, and school characteristics that are critical in predicting math achievement among 8th grade students. While the methods in this study do not use inferential statistics to examine math achievement, the predictive modeling techniques utilized may help us shed light on the contextual factors and/or culture that may account for differences in student math achievement as well as how analogous these modeled traits are across countries

    Solid/substance state change

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    Datasets & analysis scripts

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    Mixed results testing categorization in infants using a preferential-looking paradigm on zoom

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    The link between language and cognition is evident in the first months of life. As early as three months of age, a parent labeling an object that the infant is looking at influences the infants’ thoughts about those objects (Ferry et al., 2010). Prior research investigating this link between language and cognition has found successful categorization with 6- and 12-month-olds using face-to-face methodologies via preferential-looking paradigms (Fulkerson & Waxman, 2007). Due to COVID, we sought to validate an online counterpart of the categorization task. In Experiment 1, we found that language labels facilitated object categorization for 10- to 12-month-old infants. In contrast, a control condition that presented the same labeling phrases played in reverse did not facilitate categorization. In Experiment 2, we found that labels did not facilitate object categorization among 6-month-old infants. In sum, the advantage of online infant testing may be restricted to older ages
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