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Chenxi Jiang; Zhenzhong Chen; Jeremy M. Wolfe – Cognitive Research: Principles and Implications, 2024
Previous work has demonstrated similarities and differences between aerial and terrestrial image viewing. Aerial scene categorization, a pivotal visual processing task for gathering geoinformation, heavily depends on rotation-invariant information. Aerial image-centered research has revealed effects of low-level features on performance of various…
Descriptors: Geography, Photography, Classification, Data Collection
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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
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Rivka Gadot; Dina Tsybulsky – Smart Learning Environments, 2025
Critical thinking (CT) consists of a deliberate and reflective process that can lead to informed decisions. It involves scrutinizing the trustworthiness and consistency of underlying assumptions, the sources of data, and the validity of other information. CT embodies deliberate, self-regulated judgment incorporating cognitive abilities such as…
Descriptors: Critical Thinking, Data Collection, Information Management, Decision Making Skills
Paulina Berríos; Estefanía Álvarez; Karen Gutiérrez; Antonia Santos – Association for Institutional Research, 2024
This article delves into the challenges of institutional data collection processes in higher education, particularly regarding diversity reporting. The study this article is based on focuses on enhancing inclusivity by introducing a nonbinary sex category into the institutional data of a distinguished Chilean public university. In the Chilean…
Descriptors: Gender Identity, LGBTQ People, Classification, Data Collection
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Dana Garbarski; Jennifer Dykema; Cameron P. Jones; Tiffany S. Neman; Nora Cate Schaeffer; Dorothy Farrar Edwards – Field Methods, 2024
Ethnoracial identity refers to the racial and ethnic categories that people use to classify themselves and others. How it is measured in surveys has implications for understanding inequalities. Yet how people self-identify may not conform to the categories standardized survey questions use to measure ethnicity and race, leading to potential…
Descriptors: Ethnicity, Racial Identification, Classification, Error of Measurement
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Sijia Huang; Li Cai – Journal of Educational and Behavioral Statistics, 2024
The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called…
Descriptors: Classification, Data Collection, Data Analysis, Item Response Theory
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Leher Singh; Mihaela D. Barokova; Heidi A. Baumgartner; Diana C. Lopera-Perez; Paul Okyere Omane; Mark Sheskin; Francis L. Yuen; Yang Wu; Katherine J. Alcock; Elena C. Altmann; Marina Bazhydai; Alexandra Carstensen; Kin Chung Jacky Chan; Hu Chuan-Peng; Rodrigo Dal Ben; Laura Franchin; Jessica E. Kosie; Casey Lew-Williams; Asana Okocha; Tilman Reinelt; Tobias Schuwerk; Melanie Soderstrom; Angeline S. M. Tsui; Michael C. Frank – Developmental Psychology, 2024
Culture is a key determinant of children's development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants' demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child…
Descriptors: Data Collection, Young Children, Demography, Cultural Traits
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Wonkyung Choi; Jun Jo; Geraldine Torrisi-Steele – International Journal of Adult Education and Technology, 2024
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of…
Descriptors: College Students, Data Analysis, Data Collection, Learning Analytics