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Wenyi Lu; Joseph Griffin; Troy D. Sadler; James Laffey; Sean P. Goggins – Journal of Learning Analytics, 2025
Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…
Descriptors: Game Based Learning, Predictor Variables, Evaluation Methods, Educational Games
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Bowen, Natasha K.; Lucio, Robert; Patak-Pietrafesa, Michele; Bowen, Gary L. – Children & Schools, 2020
To support student success effectively, school teams need information on known predictors of youth behavior and academic performance. In contrast to measures of behavioral and academic outcomes that are commonly relied on in schools, the School Success Profile (SSP) for middle and high school students provides comprehensive information on…
Descriptors: Success, Predictor Variables, Behavior, Expectation
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Göktepe Körpeoglu, Seda; Göktepe Yildiz, Sevda – Education and Information Technologies, 2023
Examining students' attitudes towards STEM (science, technology, engineering, and mathematics) fields starting from middle school level is important in their career choices and future planning. However, there is a need to investigate which variables affect students' attitudes towards STEM. Here, we aimed to estimate middle school students'…
Descriptors: Comparative Analysis, Algorithms, Data Collection, Student Attitudes
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Ethan R. Van Norman; Emily R. Forcht – Journal of Education for Students Placed at Risk, 2024
This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…
Descriptors: Data Collection, Predictive Measurement, Predictive Validity, Predictor Variables
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Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
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Jing Liu; Julie Cohen – Educational Evaluation and Policy Analysis, 2021
Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers. Using nearly 1,000 word-to-word transcriptions of fourth- and fifth-grade English language arts classes, we apply novel text-as-data methods to develop automated measures of teaching to complement classroom observations…
Descriptors: Grade 4, Grade 5, Language Arts, Elementary School Teachers
Jing Liu; Julie Cohen – Annenberg Institute for School Reform at Brown University, 2020
Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers, but conventional classroom observations are costly, prone to rater bias, and hard to implement at scale. Using nearly 1,000 word-to-word transcriptions of 4th- and 5th-grade English language arts classes, we apply novel…
Descriptors: Grade 4, Grade 5, Language Arts, Elementary School Teachers
Yang, Dandan; Zargar, Elham; Adams, Ashley Marie; Day, Stephanie L.; Connor, Carol McDonald – Assessment for Effective Intervention, 2021
Stealth assessment has been successfully embedded in educational games to measure students' learning in an unobtrusive and supportive way. This study explored the possibility of applying stealth assessment in a digital reading platform and sought to identify potential in-system indicators of students' digital learning outcomes. Utilizing the user…
Descriptors: Electronic Publishing, Books, Computer Assisted Instruction, Reading Processes
Chang, Hedy N.; Gee, Kevin; Hennessy, Briana; Alexandro, David; Gopalakrishnan, Ajit – Attendance Works, 2021
This report describes how Connecticut took steps to collect consistent attendance data by learning mode -- remote, in-person and hybrid -- and publicly released data in a timely manner during the pandemic. For example, the Connecticut State Department of Education (CSDE) agreed upon a standard definition of attendance -- showing up to school for…
Descriptors: Attendance, COVID-19, Pandemics, Data Collection
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Rouse, Heather; Goudie, Anthony; Rettiganti, Mallik; Leath, Katherine; Riser, Quentin; Thompson, Joseph – Journal of School Health, 2019
Background: We examined prevalence, incidence, and trajectory of obesity from kindergarten through grade 8 in one of the first states to implement annual surveillance. Methods: Participants included 16,414 children enrolled in kindergarten in Arkansas in 2004 with complete body mass index (BMI) measurements in kindergarten and eighth grade.…
Descriptors: Incidence, Longitudinal Studies, Obesity, Kindergarten
Geiser, Kristin; Fehrer, Kendra; Pyne, Jaymes; Gerstein, Amy; Harrison, Vicki; Joshi, Shashank – John W. Gardner Center for Youth and Their Communities, 2019
According to national indicators of adolescent health and well-being, the most significant health issues young people face are related to mental health. In San Mateo County, a recent report on adolescent health frames the prevalence of mental health needs among public school students as "staggering." Both locally and nationally, schools…
Descriptors: Adolescents, Child Health, Well Being, Mental Health
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Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis
Geiser, Kristin; Fehrer, Kendra; Pyne, Jaymes; Gerstein, Amy; Harrison, Vicki; Joshi, Shashank – John W. Gardner Center for Youth and Their Communities, 2019
According to national indicators of adolescent health and well-being, mental health is one of the most significant health issues young people face. Since mental health is linked to other aspects of health and well-being, undiagnosed and untreated mental health conditions can negatively impact a young person's social-emotional health, academic…
Descriptors: Adolescents, Child Health, Well Being, Mental Health
Wang, Yutao; Heffernan, Neil T.; Heffernan, Cristina – Grantee Submission, 2015
The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first…
Descriptors: Majors (Students), Affective Behavior, Psychological Patterns, Predictor Variables
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Knezek, Gerald; Christensen, Rhonda; Tyler-Wood, Tandra; Gibson, David – Journal of STEM Education: Innovations and Research, 2015
Data gathered from 325 middle school students in four U.S. states indicate that both male (p < 0.0005, RSQ = 0.33) and female (p < 0.0005, RSQ = 0.36) career aspirations for "being a scientist" are predictable based on knowledge of dispositions toward mathematics, science and engineering, plus self-reported creative tendencies. For…
Descriptors: Middle School Students, Gender Differences, STEM Education, Occupational Aspiration
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