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Savi, Alexander O.; Deonovic, Benjamin E.; Bolsinova, Maria; van der Maas, Han L. J.; Maris, Gunter K. J. – Journal of Educational Data Mining, 2021
In learning, errors are ubiquitous and inevitable. As these errors may signal otherwise latent cognitive processes, tutors--and students alike--can greatly benefit from the information they provide. In this paper, we introduce and evaluate the Systematic Error Tracing (SET) model that identifies the possible causes of systematically observed…
Descriptors: Learning Processes, Cognitive Processes, Error Patterns, Models
Fife, James H.; James, Kofi; Peters, Stephanie – ETS Research Report Series, 2020
The concept of variability is central to statistics. In this research report, we review mathematics education research on variability and, based on that review and on feedback from an expert panel, propose a learning progression (LP) for variability. The structure of the proposed LP consists of 5 levels of sophistication in understanding…
Descriptors: Mathematics Education, Statistics Education, Feedback (Response), Research Reports
Kiray, S. Ahmet; Gok, Bilge; Bozkir, A. Selman – Journal of Education in Science, Environment and Health, 2015
The purpose of this article is to identify the order of significance of the variables that affect science and mathematics achievement in middle school students. For this aim, the study deals with the relationship between science and math in terms of different angles using the perspectives of multiple causes-single effect and of multiple…
Descriptors: Science Achievement, Information Retrieval, Data Analysis, Middle School Students
Curcio, Frances R. – 1989
This book provides elementary and middle school teachers with practical ideas on the use and teaching of graphs. Five sections consist of: (1) "Graphs--What Are They and How Are They Used?"; (2) "Levels of Graph Comprehension"; (3) "Collecting, Organizing, and Analyzing Data"; (4) "Constructing, Interpreting, and Writing about Graphs"; and (5)…
Descriptors: Comprehension, Data Analysis, Data Interpretation, Elementary Education
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement

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