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Anjali Adukia; Alex Eble; Emileigh Harrison; Hakizumwami Birali Runesha; Teodora Szasz – Grantee Submission, 2023
Books shape how children learn about society and norms, in part through representation of different characters. We use computational tools to characterize representation in children's books widely read in homes, classrooms, and libraries over the last century, and describe economic forces that may contribute to these patterns. We introduce new…
Descriptors: Self Concept, Racism, Gender Bias, Childrens Literature
Anjali Adukia; Alex Eble; Emileigh Harrison; Hakizumwami Birali Runesha; Teodora Szasz – Society for Research on Educational Effectiveness, 2021
Background: Education teaches children about the world, its people, and their place in it. Much of this happens through the curricular materials we present to children, particularly the books they read in school and at home (Giroux, 1981; Apple and Christian-Smith, 1991; Jansen, 1997; Van Kleeck, Stahl and Bauer, 2003; Steele, 2010). How different…
Descriptors: Self Concept, Racism, Gender Bias, Childrens Literature
Kumar, Chandan Jyoti; Das, Priti Rekha – International Journal of Developmental Disabilities, 2022
Autism Spectrum Disorder (ASD) is a highly heterogeneous set of neurodevelopmental disorders with the global prevalence estimates of 2.20%, according to DSM5 criteria. With the advancements of technology and availability of huge amount of data, assistive tools for diagnosis of ASD are being developed using machine learning techniques. The present…
Descriptors: Autism Spectrum Disorders, Clinical Diagnosis, Artificial Intelligence, Observation
Yanagiura, Takeshi – Community College Review, 2023
Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…
Descriptors: Community Colleges, Community College Students, Educational Indicators, Outcomes of Education
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students
Cattell, Lindsay; Bruch, Julie – Regional Educational Laboratory Mid-Atlantic, 2021
This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students. Districts use early warning systems to target resources to the most at-risk students and intervene before students drop out. Schools want to ensure the early warning system accurately identifies…
Descriptors: At Risk Students, Identification, Artificial Intelligence, Dropout Prevention
EdChoice, 2023
This poll was conducted between August 18-August 27, 2023 among a national sample of 1,000 Teens. The interviews were conducted online and the data were weighted to approximate a target sample of Teens based on gender, age, race, and region. Results from the full survey have a measure of precision of plus or minus 3.3 percentage points. Among the…
Descriptors: Adolescents, Gender Differences, Age Differences, Racial Differences
Yang, Jie; DeVore, Seth; Hewagallage, Dona; Miller, Paul; Ryan, Qing X.; Stewart, John – Physical Review Physics Education Research, 2020
Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational…
Descriptors: Artificial Intelligence, Man Machine Systems, Identification, At Risk Students
Balyan, Renu; Crossley, Scott A.; Brown, William, III; Karter, Andrew J.; McNamara, Danielle S.; Liu, Jennifer Y.; Lyles, Courtney R.; Schillinger, Dean – Grantee Submission, 2019
Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate…
Descriptors: Patients, Literacy, Health Services, Profiles