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Weissman, M. B. – Physical Review Physics Education Research, 2021
Sound educational policy recommendations require valid estimates of causal effects, but observational studies in physics education research sometimes have loosely specified causal hypotheses. The connections between the observational data and the explicit or implicit causal conclusions are sometimes misstated. The link between the causal…
Descriptors: Physics, Science Education, Attribution Theory, Educational Policy
Garcia Moreno-Esteva, Enrique; Kervinen, Anttoni; Hannula, Markku S.; Uitto, Anna – Education Sciences, 2020
In this article we discuss, as a proof of concept, how a network model can be used to analyse gaze tracking data coming from a preliminary experiment carried out in a biodiversity education research project. We discuss the network model, a simple directed graph, used to represent the gaze tracking data in a way that is meaningful for the study of…
Descriptors: Biology, Science Instruction, Biodiversity, Networks
Aguilar, Stephen J. – Journal of Research on Technology in Education, 2018
This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory--a prominent theory of students' academic motivation--and utilizes interviews (n = 60) to investigate how students at risk…
Descriptors: Comparative Analysis, Visualization, At Risk Students, College Students
Rakap, Salih; Snyder, Patricia; Pasia, Cathleen – Behavioral Disorders, 2014
Debate is occurring about which result interpretation aides focused on examining the experimental effect should be used in single-subject experimental research. In this study, we examined seven nonoverlap methods and compared results using each method to judgments of two visual analysts. The data sources for the present study were 36 studies…
Descriptors: Educational Research, Experiments, Research Problems, Research Methodology
Lefebre, Elizabeth; Fabrizio, Michael; Merbitz, Charles – Journal of Precision Teaching and Celeration, 2008
Although behavior analysis relies primarily on visual inspection for interpreting data, previous research shows that the method of display can influence the judgments. In the current study, 26 Board-Certified Behavior Analysts reviewed two data sets displayed on each of three methods--equal-interval graphs, tables, and Standard Celeration…
Descriptors: Educational Research, Data Interpretation, Efficiency, Comparative Analysis
Smoot, Sharene; And Others – 1990
This study was designed to find: a suitable effect size (ES) measure or common metric (CM) for comparing the results of a set of single subject research (SSR) studies; and an easy way to convert the published graphs back into raw data from which ESs could be calculated. To meet the first objective, three possible formulas for measuring treatment…
Descriptors: Behavior Disorders, Comparative Analysis, Computer Uses in Education, Educational Research
Educational Research Service, Arlington, VA. – 1979
The composite indicator, published annually by the Educational Research Service (ERS), was designed to reflect overall changes in average salaries and wages paid by public school systems. The purpose of this book is to describe the nature of the indicator, how it is compiled on a national and state basis, and how local school systems can compile…
Descriptors: Administrators, Comparative Analysis, Educational Research, Elementary Secondary Education
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection

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