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How Much Is Too Much Time Spent on Homework: An Exploratory Study Based on a Bayesian Multilevel Piecewise Model with a Random Change Point
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Hao Zhou; Jian Liu; Yueyang Shao; Tian Yanyan – Educational Studies, 2025
In this study, we applied multilevel piecewise linear regression with a random-effects model under a Bayesian estimation framework to a total of 5,072 7th grade students in 35 schools to search for an optimum time in terms of the effect of homework time on student academic achievement. First, we found a nonlinear relationship between time spent on…
Descriptors: Foreign Countries, Grade 7, Homework, Time on Task
A New Student Modeling Technique with Convolutional Neural Networks: LearnerPrints
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Aydogdu, Seyhmus – Journal of Educational Computing Research, 2021
Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs),…
Descriptors: Mathematical Models, Artificial Intelligence, Bayesian Statistics, Learning Processes
Flexible Bayesian Methods for High Dimensional Data
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Enakshi Saha – ProQuest LLC, 2021
We study flexible Bayesian methods that are amenable to a wide range of learning problems involving complex high dimensional data structures, with minimal tuning. We consider parametric and semiparametric Bayesian models, that are applicable to both static and dynamic data, arising from a multitude of areas such as economics, finance and…
Descriptors: Bayesian Statistics, Probability, Nonparametric Statistics, Data Analysis
Flexible Bayesian Models for Inferences from Coarsened, Group-Level Achievement Data
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Lockwood, J. R.; Castellano, Katherine E.; Shear, Benjamin R. – Journal of Educational and Behavioral Statistics, 2018
This article proposes a flexible extension of the Fay--Herriot model for making inferences from coarsened, group-level achievement data, for example, school-level data consisting of numbers of students falling into various ordinal performance categories. The model builds on the heteroskedastic ordered probit (HETOP) framework advocated by Reardon,…
Descriptors: Bayesian Statistics, Mathematical Models, Statistical Inference, Computation
Using the Stan Program for Bayesian Item Response Theory
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Luo, Yong; Jiao, Hong – Educational and Psychological Measurement, 2018
Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the…
Descriptors: Bayesian Statistics, Item Response Theory, Probability, Computer Software
Using Multisite Experiments to Study Cross-Site Variation in Treatment Effects: A Hybrid Approach with Fixed Intercepts and A Random Treatment Coefficient
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Bloom, Howard S.; Raudenbush, Stephen W.; Weiss, Michael J.; Porter, Kristin – Journal of Research on Educational Effectiveness, 2017
The present article considers a fundamental question in evaluation research: "By how much do program effects vary across sites?" The article first presents a theoretical model of cross-site impact variation and a related estimation model with a random treatment coefficient and fixed site-specific intercepts. This approach eliminates…
Descriptors: Evaluation Research, Program Evaluation, Welfare Services, Employment