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Jennifer L. Chiu; James P. Bywater; Tugba Karabiyik; Alejandra Magana; Corey Schimpf; Ying Ying Seah – Journal of Science Education and Technology, 2024
Despite an increasing focus on integrating engineering design in K-12 settings, relatively few studies have investigated how to support students to engage in systematic processes to optimize the designs of their solutions. Emerging learning technologies such as computational models and simulations enable rapid feedback to learners about their…
Descriptors: Engineering, Middle School Students, High School Students, Building Design
Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
Christine G. Casey, Editor – Centers for Disease Control and Prevention, 2024
The "Morbidity and Mortality Weekly Report" ("MMWR") series of publications is published by the Office of Science, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services. Articles included in this supplement are: (1) Overview and Methods for the Youth Risk Behavior Surveillance System --…
Descriptors: High School Students, At Risk Students, Health Behavior, National Surveys
Bondaryk, Leslie G.; Hsi, Sherry; Van Doren, Seth – IEEE Transactions on Learning Technologies, 2021
Sensor systems have the potential to make abstract science phenomena concrete for K-12 students. Internet of Things (IoT) sensor systems provide a variety of benefits for modern classrooms, creating the opportunity for global data production, orienting learners to the opportunities and drawbacks of distributed sensor and control systems, and…
Descriptors: Internet, Systems Development, Computer Uses in Education, Secondary School Science
Pangrazio, Luci; Selwyn, Neil – Pedagogy, Culture and Society, 2021
The ongoing 'datafication' of contemporary society has a number of implications for schools and schooling. One is the increasing calls for schools to help develop young people's understandings about the role that digital data now plays in their everyday lives -- especially in terms of the 'data economy' and 'surveillance capitalism'. Reporting on…
Descriptors: Data Collection, Data Analysis, Technology Uses in Education, Data Processing
Duprey, Michael A.; Pratt, Daniel J.; Wilson, David H.; Jewell, Donna M.; Brown, Derick S.; Caves, Lesa R.; Kinney, Satkartar K.; Mattox, Tiffany L.; Ritchie, Nichole Smith; Rogers, James E.; Spagnardi, Colleen M.; Wescott, Jamie D. – National Center for Education Statistics, 2020
This data file documentation accompanies new data files for the High School Longitudinal Study of 2009 (HSLS:09) Postsecondary Education Transcript Study and Student Financial Aid Records Collection (PETS-SR). HSLS:09 follows a nationally representative sample of students who were ninth-graders in fall 2009 from high school into postsecondary…
Descriptors: Longitudinal Studies, High School Students, Sampling, Data Collection
P. Janelle McFeetors – Sage Research Methods Cases, 2016
This case study describes an experience of using constructivist grounded theory to analyze data. The project investigated how high school students improved their approaches to learning mathematics. Over 4 months, students participated in processes which supported their learning while simultaneously generating data, including interactive writing,…
Descriptors: High School Students, Mathematics Education, Data Analysis, Data Interpretation
Liu, Ran; Stamper, John; Davenport, Jodi – Grantee Submission, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Snow, Erica L. – International Educational Data Mining Society, 2015
Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…
Descriptors: Intelligent Tutoring Systems, Models, Individualized Instruction, Needs Assessment
Ingels, Steven J.; Pratt, Daniel J.; Jewell, Donna M.; Mattox, Tiffany; Dalton, Ben; Rosen, Jeffrey; Lauff, Erich; Hill, Jason – National Center for Education Statistics, 2012
This report describes the methodologies and results of the third follow-up Education Longitudinal Study of 2002 (ELS:2002/12) field test which was conducted in the summer of 2011. The field test report is divided into six chapters: (1) Introduction; (2) Field Test Survey Design and Preparation; (3) Data Collection Procedures and Results; (4) Field…
Descriptors: Longitudinal Studies, Field Tests, Followup Studies, Surveys
Rodriguez, Sheila M.; Estacion, Angela – Regional Educational Laboratory Northeast & Islands, 2014
As the name indicates, the College Readiness Data Catalog Tool focuses on identifying data that can indicate a student's college readiness. While college readiness indicators may also signal career readiness, many states, districts, and other entities, including the U.S. Virgin Islands (USVI), do not systematically collect career readiness…
Descriptors: College Readiness, Data, Educational Indicators, Data Collection
Nord, C.; Hicks, L.; Hoover, K.; Jones, M.; Lin, A.; Lyons, M.; Perkins, R.; Roey, S.; Rust, K.; Sickles, D. – National Center for Education Statistics, 2011
This user's guide documents the procedures used to collect, process, and summarize data from the 2009 High School Transcript Study (HSTS 2009). Chapters detail the sampling of schools and graduates (chapters 2 and 3), data collection procedures (chapter 4), data processing procedures (chapter 5), and weighting procedures (chapter 6). Chapter 7…
Descriptors: High School Graduates, Academic Records, National Competency Tests, Questionnaires
Levy, Sharona T.; Wilensky, Uri – Computers & Education, 2011
This study lies at an intersection between advancing educational data mining methods for detecting students' knowledge-in-action and the broader question of how conceptual and mathematical forms of knowing interact in exploring complex chemical systems. More specifically, it investigates students' inquiry actions in three computer-based models of…
Descriptors: Test Content, Mathematical Models, Prior Learning, Data Processing
Omar, Ahmad Fairuz; MatJafri, Mohd Zubir – Physics Education, 2011
The Swift Turbidity Marker is an optical instrument developed to measure the level of water turbidity. The components and configuration selected for the system are based on common turbidity meter design concepts but use a simplified methodology to produce rapid turbidity measurements. This work is aimed at high school physics students and is the…
Descriptors: Optics, International Organizations, Data Analysis, Mathematical Applications
Feinberg, Dave – Computer Science Education, 2007
This paper presents a simple 4 bit computer processor design that may be built using TTL chips for less than $65. In addition to describing the processor itself in detail, we discuss our experience using the laboratory kit and its associated machine instruction set to teach computer architecture to high school students. (Contains 3 figures and 5…
Descriptors: Computer System Design, Data Processing, Learning Modules, High School Students
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