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Carly Oddleifson; Stephen Kilgus; David A. Klingbeil; Alexander D. Latham; Jessica S. Kim; Ishan N. Vengurlekar – Grantee Submission, 2025
The purpose of this study was to conduct a conceptual replication of Pendergast et al.'s (2018) study that examined the diagnostic accuracy of a nomogram procedure, also known as a naive Bayesian approach. The specific naive Bayesian approach combined academic and social-emotional and behavioral (SEB) screening data to predict student performance…
Descriptors: Bayesian Statistics, Accuracy, Social Emotional Learning, Diagnostic Tests
Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Coux, Mickael; Xiao, ZhiMin; Shkedy, Ziv; Kasim, Adetayo – Journal of Experimental Education, 2022
Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in education have used the standardised mean difference as a measure of the impact of interventions. This…
Descriptors: Program Effectiveness, Intervention, Multivariate Analysis, Bayesian Statistics
DiCerbo, Kristen – Learning, Media and Technology, 2016
The volume of data that can be captured and stored from students' everyday interactions with digital environments allows for the creation of models of student knowledge, skills, and attributes unobtrusively. However, models and techniques for transforming these data into information that is useful for educators have not been established. This…
Descriptors: Bayesian Statistics, Educational Technology, Electronic Learning, Learning Processes
Phelan, Julia; Ing, Marsha; Nylund-Gibson, Karen; Brown, Richard S. – Journal of STEM Education: Innovations and Research, 2017
This study extends current research by organizing information about students' expectancy-value achievement motivation, in a way that helps parents and teachers identify specific entry points to encourage and support students' science aspirations. This study uses latent class analysis to describe underlying differences in ability beliefs, task…
Descriptors: Self Concept, Science Instruction, Middle School Students, Multivariate Analysis
Stone, Clement A.; Tang, Yun – Practical Assessment, Research & Evaluation, 2013
Propensity score applications are often used to evaluate educational program impact. However, various options are available to estimate both propensity scores and construct comparison groups. This study used a student achievement dataset with commonly available covariates to compare different propensity scoring estimation methods (logistic…
Descriptors: Comparative Analysis, Probability, Sample Size, Program Evaluation
Rai, Dovan; Gong, Yue; Beck, Joseph E. – International Working Group on Educational Data Mining, 2009
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its…
Descriptors: Data Analysis, Statistical Analysis, Probability, Models