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Xing, Wanli; Li, Chenglu; Chen, Guanhua; Huang, Xudong; Chao, Jie; Massicotte, Joyce; Xie, Charles – Journal of Educational Computing Research, 2021
Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide…
Descriptors: Engineering Education, Design, Feedback (Response), Student Evaluation
Allen, Jeff – Applied Measurement in Education, 2017
Using a sample of schools testing annually in grades 9-11 with a vertically linked series of assessments, a latent growth curve model is used to model test scores with student intercepts and slopes nested within school. Missed assessments can occur because of student mobility, student dropout, absenteeism, and other reasons. Missing data…
Descriptors: Achievement Gains, Academic Achievement, Growth Models, Scores
Foorman, Barbara R.; Petscher, Yaacov – Society for Research on Educational Effectiveness, 2011
In Florida, mean proficiency scores are reported on the Florida Comprehensive Achievement Test (FCAT) as well as recommended learning gains from the developmental scale score. Florida now has another within-year measure of growth in reading comprehension from the Florida Assessments for Instruction in Reading (FAIR). The FAIR reading comprehension…
Descriptors: Achievement Tests, Reading Comprehension, Reading Tests, Scores
Zhang, Zhiyong; Lai, Keke; Lu, Zhenqiu; Tong, Xin – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…
Descriptors: Structural Equation Models, Bayesian Statistics, Statistical Inference, Statistical Distributions
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Reading Comprehension, Reading Achievement, Elementary School Students, Secondary School Students
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Response to Intervention, Achievement Gains, High Stakes Tests, Prediction
Braeken, Johan; Blömeke, Sigrid – Assessment & Evaluation in Higher Education, 2016
Using data from the international Teacher Education and Development Study: Learning to Teach Mathematics (TEDS-M), the measurement equivalence of teachers' beliefs across countries is investigated for the case of "mathematics-as-a fixed-ability". Measurement equivalence is a crucial topic in all international large-scale assessments and…
Descriptors: Comparative Analysis, Bayesian Statistics, Test Bias, Teacher Education
Pardos, Zachary A.; Heffernan, Neil T. – International Working Group on Educational Data Mining, 2009
Researchers who make tutoring systems would like to know which sequences of educational content lead to the most effective learning by their students. The majority of data collected in many ITS systems consist of answers to a group of questions of a given skill often presented in a random sequence. Following work that identifies which items…
Descriptors: Data Analysis, Bayesian Statistics, Statistical Analysis, Problem Sets
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries