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Chen Zhong; J. B. Kim – Journal of Information Systems Education, 2024
Data Analytics has emerged as an essential skill for business students, and several tools are available to support their learning in this area. Due to the students' lack of programming skills and the perceived complexity of R, many business analytics courses employ no-code analytical software like IBM SPSS Modeler. Nonetheless, generative…
Descriptors: Business Education, Regression (Statistics), Programming, Artificial Intelligence
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Weirich, Sebastian; Haag, Nicole; Hecht, Martin; Böhme, Katrin; Siegle, Thilo; Lüdtke, Oliver – Large-scale Assessments in Education, 2014
Background: In order to measure the proficiency of person populations in various domains, large-scale assessments often use marginal maximum likelihood IRT models where person proficiency is modelled as a random variable. Thus, the model does not provide proficiency estimates for any single person. A popular approach to derive these proficiency…
Descriptors: Measurement, Item Response Theory, Measurement Techniques, Evaluation Methods
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Anderson, Ariana; Locke, Jill; Kretzmann, Mark; Kasari, Connie – Autism: The International Journal of Research and Practice, 2016
Although children with autism spectrum disorder are frequently included in mainstream classrooms, it is not known how their social networks change compared to typically developing children and whether the factors predictive of this change may be unique. This study identified and compared predictors of social connectivity of children with and…
Descriptors: Social Networks, Network Analysis, Elementary School Students, Autism
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Haber, Mason G.; Mazzotti, Valerie L.; Mustian, April L.; Rowe, Dawn A.; Bartholomew, Audrey L.; Test, David W.; Fowler, Catherine H. – Review of Educational Research, 2016
Students with disabilities experience poorer post-school outcomes compared with their peers without disabilities. Existing experimental literature on "what works" for improving these outcomes is rare; however, a rapidly growing body of research investigates correlational relationships between experiences in school and post-school…
Descriptors: Meta Analysis, Predictor Variables, Success, Postsecondary Education
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Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses
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Beers, Scott F.; Quinlan, Thomas; Harbaugh, Allen G. – Reading and Writing: An Interdisciplinary Journal, 2010
This study employed eyetracking technology to investigate adolescent students' reading processes as they composed and to explore relationships between these reading processes and text quality. A sample of 32 adolescent students composed narrative and expository texts while eyetracking equipment recorded their eye movements. Eye movements upon a…
Descriptors: Eye Movements, Technology Uses in Education, Adolescents, Reading Processes
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O'Donnell, Melissa; Nassar, Natasha; Leonard, Helen; Mathews, Richard; Patterson, Yvonne; Stanley, Fiona – Child Abuse & Neglect: The International Journal, 2010
Objectives: To investigate the prevalence, trends, and characteristics of maltreatment and assault related hospital admissions and deaths among children; and identify common injuries and conditions associated with these admissions using routinely collected morbidity and mortality data. Methods: A retrospective cohort study of all children aged…
Descriptors: Child Abuse, Child Neglect, Incidence, Hospitals
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Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss – Journal of Statistics Education, 2004
In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…
Descriptors: Introductory Courses, Statistics, Class Activities, Data Collection