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Download full textHsu, Chia-Ling; Chen, Yi-Hsin; Wu, Yi-Jhen – Practical Assessment, Research & Evaluation, 2023
Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…
Descriptors: Bayesian Statistics, Computation, International Assessment, Achievement Tests
Marianthi Grizioti; Chronis Kynigos – Informatics in Education, 2024
Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying…
Descriptors: Computation, Thinking Skills, Data Science, Classification
Direct linkWells, Craig S.; Sireci, Stephen G. – Applied Measurement in Education, 2020
Student growth percentiles (SGPs) are currently used by several states and school districts to provide information about individual students as well as to evaluate teachers, schools, and school districts. For SGPs to be defensible for these purposes, they should be reliable. In this study, we examine the amount of systematic and random error in…
Descriptors: Growth Models, Reliability, Scores, Error Patterns
Suk, Youmi; Kim, Jee-Seon; Kang, Hyunseung – Journal of Educational and Behavioral Statistics, 2021
There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…
Descriptors: Artificial Intelligence, Statistical Analysis, Statistical Inference, Classification
Levin, Nathan A. – Journal of Educational Data Mining, 2021
The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…
Descriptors: Learning Analytics, Data Collection, Competition, Prediction
Huang, Hsin-Mei E. – EURASIA Journal of Mathematics, Science and Technology Education, 2020
The current study examined the effects of grade level and object size on the ability to estimate the measurement of objects. Fifth- (n = 198) and sixth-grade (n = 208) students and freshman undergraduate students (n = 71) from Taipei city, Taiwan, participated in the study. The findings indicated a tendency for measurement estimation ability to…
Descriptors: Instructional Program Divisions, Grade 5, Grade 6, College Freshmen
Alonzo, Julie; Anderson, Daniel – Behavioral Research and Teaching, 2018
In response to a request for additional analyses, in particular reporting confidence intervals around the results, we re-analyzed the data from prior studies. This supplementary report presents the results of the additional analyses addressing classification accuracy, reliability, and criterion-related validity evidence. For ease of reference, we…
Descriptors: Curriculum Based Assessment, Computation, Statistical Analysis, Accuracy
Alonzo, Julie; Anderson, Daniel – Behavioral Research and Teaching, 2018
In response to a request for additional analyses, in particular reporting confidence intervals around the results, we re-analyzed the data from prior studies. This supplementary report presents the results of the additional analyses addressing classification accuracy, reliability, and criterion-related validity evidence. For ease of reference, we…
Descriptors: Curriculum Based Assessment, Computation, Statistical Analysis, Classification
Maf'ulah, Syarifatul; Juniati, Dwi; Siswono, Tatag Yuli Eko – Educational Research and Reviews, 2016
The fact that there is no much study on reversibility is one of reason this study was conducted. Others, the importance of reversibility is also being researcher's motivation for focusing pupils' reversibility. On the other hand, the concern on pupils' reversibility is a major concern. The objective of this research is to identify errors done by…
Descriptors: Foreign Countries, Elementary School Students, Grade 5, Error Patterns
Rutkowski, Leslie – Applied Measurement in Education, 2014
Large-scale assessment programs such as the National Assessment of Educational Progress (NAEP), Trends in International Mathematics and Science Study (TIMSS), and Programme for International Student Assessment (PISA) use a sophisticated assessment administration design called matrix sampling that minimizes the testing burden on individual…
Descriptors: Measurement, Testing, Item Sampling, Computation
Tipton, Elizabeth – Journal of Educational and Behavioral Statistics, 2013
As a result of the use of random assignment to treatment, randomized experiments typically have high internal validity. However, units are very rarely randomly selected from a well-defined population of interest into an experiment; this results in low external validity. Under nonrandom sampling, this means that the estimate of the sample average…
Descriptors: Generalization, Experiments, Classification, Computation
Wilkerson-Jerde, Michelle Hoda – Educational Technology Research and Development, 2014
There are increasing calls to prepare K-12 students to use computational tools and principles when exploring scientific or mathematical phenomena. The purpose of this paper is to explore whether and how constructionist computer-supported collaborative environments can explicitly engage students in this practice. The Categorizer is a…
Descriptors: Computation, Classification, Computer Uses in Education, Cooperative Learning
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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
