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Joshua Angrist; Peter Hull; Russell Legate-Yang; Parag A. Pathak; Christopher R. Walters – National Bureau of Economic Research, 2025
School districts increasingly gauge school quality with surveys that ask about school climate and student engagement. We use data from New York City's middle and high schools to compare the long-run predictive validity of surveys with that of conventional test score value-added models (VAMs). Our analysis leverages the New York school match, which…
Descriptors: School Surveys, Middle Schools, High Schools, Prediction
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Yaosheng Lou; Kimberly F. Colvin – Discover Education, 2025
Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…
Descriptors: Educational Research, Data Collection, Performance, Prediction
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Faming Wang; Ronnel B. King; Lingyi Fu; Ching-Sing Chai; Shing On Leung – International Journal of Science Education, 2024
Resilient students attain high levels of academic achievement despite the presence of chronic socioeconomic disadvantage. Identifying factors that promote resilience in the domain of science is crucial to making equitable and high-quality science education accessible for all students. Rooted in the opportunity-propensity framework, this study…
Descriptors: Resilience (Psychology), Foreign Countries, Grade 8, Science Education
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Saeed Kabiri; Christopher M. Donner; Seyyedeh Masoomeh Shadmanfaat; Mohammad Mahdi Rahmati – International Journal of Bullying Prevention, 2024
Bullying, particularly among teenagers and young adults, is one of the most important issues facing school communities. At its very heart, this issue speaks to a troubling form of deviant behavior. When students engage in bullying behaviors, the effects are felt far beyond that of the direct victim. As such, it is important to investigate the…
Descriptors: High School Students, Student Attitudes, Bullying, Foreign Countries
Jessa Henderson – ProQuest LLC, 2024
Algorithms may be better at prediction than humans in a variety of contexts, but they are not perfect. A deeper understanding of the ways in which educators use and question algorithmic advice within their professional domain is needed. Educators are a particularly unique professional group, in comparison with the other groups studied in the…
Descriptors: Algorithms, Literacy, High School Teachers, Science Teachers
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Belinda Merkle; Laura Aglaia Sophia Messerer; Oliver Dickhäuser – Social Psychology of Education: An International Journal, 2024
Choosing a field of study (study major) is challenging for prospective students. However, little research has examined factors measured prior to enrollment to predict motivation and well-being in a specific study major. Based on literature on affective forecasting and person-environment fit, prospective students' well-being forecast could be such…
Descriptors: Majors (Students), Student Motivation, Well Being, Prediction
Ssebagereka, Irene Tamallie – ProQuest LLC, 2023
Organizational commitment and knowledge management are both crucial factors in the growth of an organization. The quantitative, correlational-predictive study was to examine if and to what extent the constituents of KM together predicted organizational commitment for high school employees in USA. Based on the premise that the relationship of…
Descriptors: High Schools, Employees, School Personnel, Knowledge Management
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Stephen Hunter; Carla Hilario; Karen A. Patte; Scott T. Leatherdale; Roman Pabayo – Journal of School Health, 2024
Background: Income inequality is theorized to impact health. However, evidence among adolescents is limited. This study examined the association between income inequality and health-related school absenteeism (HRSA) in adolescents. Methods: Participants were adolescents (n = 74,501) attending secondary schools (n = 136) that participated in the…
Descriptors: Correlation, Social Differences, Secondary School Students, Attendance
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Linda Borger; Stefan Johansson; Rolf Strietholt – Educational Assessment, Evaluation and Accountability, 2024
PISA aims to serve as a "global yardstick" for educational success, as measured by student performance. For comparisons to be meaningful across countries or over time, PISA samples must be representative of the population of 15-year-old students in each country. Exclusions and non-response can undermine this representativeness and…
Descriptors: Achievement Tests, International Assessment, Foreign Countries, Secondary School Students
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Ifeyinwa Uke; Jazlin Ebenezer; Osman Nafiz Kaya – Research in Science Education, 2024
This mixed-methods research study aimed to observe the changes in relational conceptual changes and achievement in photosynthesis and cellular respiration in 15 seventh-grade students using the variation theory of learning, a framework for contextual distinctions, and supports the Common Knowledge Construction Model (CKCM) for science education.…
Descriptors: Grade 7, Scientific Concepts, Science Achievement, Cytology
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Okan Bulut; Tarid Wongvorachan; Surina He; Soo Lee – Discover Education, 2024
Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school…
Descriptors: High School Students, Dropouts, Identification, Man Machine Systems
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Shiyi Liu; Juan Zheng; Tingting Wang; Zeda Xu; Jie Chao; Shiyan Jiang – AERA Online Paper Repository, 2024
This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses,…
Descriptors: Learner Engagement, Artificial Intelligence, Technology Uses in Education, Language Arts
Tina Panagopoulos – ProQuest LLC, 2024
The purpose of this quantitative correlational predictive study was to examine if a predictive relationship existed between emotional intelligence and the dimensions of burnout among K-8 teachers in the United States. The trait emotional intelligence theory and the Maslach burnout model provided the foundation for the study. The sample included…
Descriptors: Correlation, Emotional Intelligence, Prediction, Teacher Burnout
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Tao Huang; Jing Geng; Yuxia Chen; Han Wang; Huali Yang; Shengze Hu – Education and Information Technologies, 2024
Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in…
Descriptors: Artificial Intelligence, Elementary Schools, Secondary Schools, Prediction
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Wudhijaya Philuek – Asian Journal of Education and Training, 2024
The objectives of this research were 1) to study the problems of stress and depression among Grade 12 students; 2) to investigate the machine learning technique in analyzing and predicting stress, depression, and academic performance among Grade 12 students; and 3) to evaluate the stress and depression prediction platform. Students from schools in…
Descriptors: Artificial Intelligence, Stress Variables, Depression (Psychology), Academic Achievement
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