Research

Re-imagining Science Education Research Toward a Language for Science Perspective

With a decade passing since the release of the Next Generation Science Standards (NGSS), it is timely to reflect and consider the extent to which the promise of science teaching and learning that values and centers learners’ varied epistemologies for scientific sensemaking has been realized. We argue that this potential, in part, lies in the hands of our science education research community becoming aware and intentional with how we situate learners’ language-related resources and practices in our work.

Author/Presenter

María González-Howard

Sage Andersen

Karina Méndez Pérez

Samuel Lee

Lead Organization(s)
Year
2024
Short Description

With a decade passing since the release of the Next Generation Science Standards (NGSS), it is timely to reflect and consider the extent to which the promise of science teaching and learning that values and centers learners’ varied epistemologies for scientific sensemaking has been realized. We argue that this potential, in part, lies in the hands of our science education research community becoming aware and intentional with how we situate learners’ language-related resources and practices in our work.

The STEM Observation Protocol Training Course

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

Author/Presenter

STEM-OP Project Team

Year
2023
Short Description

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

The STEM Observation Protocol Training Course

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

Author/Presenter

STEM-OP Project Team

Year
2023
Short Description

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

The STEM Observation Protocol Training Course

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

Author/Presenter

STEM-OP Project Team

Year
2023
Short Description

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

Classroom-Based STEM Assessment: Contemporary Issues and Perspectives

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Author/Presenter

Christopher J. Harris, Eric Wiebe, Shuchi Grover, James W. Pellegrino, Eric Banilower, Arthur Baroody, Erin Furtak, Ryan “Seth” Jones, Leanne R. Ketterlin-Geller, Okhee Lee, Xiaoming Zhai

Year
2023
Short Description

This report takes stock of what we currently know as well as what we need to know to make classroom assessment maximally beneficial for the teaching and learning of STEM subject matter in K–12 classrooms.

A Model Comparison Approach to Posterior Predictive Model Checks in Bayesian Confirmatory Factor Analysis

Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by comparing the location of an ML-based point estimate to the predictive distribution of a statistic. When the point estimate is far from the center posterior predictive distribution, model fit is poor. Not included in this approach, however, is the variability of the Maximum Likelihood (ML)-based point estimates.

Author/Presenter

Jonathan Templin

Catherine E. Mintz

Lead Organization(s)
Year
2022
Short Description

Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by comparing the location of an ML-based point estimate to the predictive distribution of a statistic. When the point estimate is far from the center posterior predictive distribution, model fit is poor. Not included in this approach, however, is the variability of the Maximum Likelihood (ML)-based point estimates. We propose a new method of PPMC based on comparing posterior predictive distributions of a hypothesized and saturated BCFA model.

A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models

Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity constraints on their item parameters, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicity constraints. A simulation study was conducted to evaluate parameter recovery of the algorithm which showed accurate estimation of model parameters.

Author/Presenter

Kazuhiro Yamaguchi

Jonathan Templin 

Lead Organization(s)
Year
2021
Short Description

Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity constraints on their item parameters, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicity constraints.

The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal).

Author/Presenter

Sedat Sen

Allan S. Cohen

Lead Organization(s)
Year
2022
Short Description

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL.

Estimation of Multidimensional Item Response Theory Models with Correlated Latent Variables Using Variational Autoencoders

Artificial neural networks with a specific autoencoding structure are capable of estimating parameters for the multidimensional logistic 2-parameter (ML2P) model in item response theory (Curi et al. in International joint conference on neural networks (IJCNN), 2019), but with limitations, such as uncorrelated latent traits. In this work, we extend variational auto encoders (VAE) to estimate item parameters and correlated latent abilities, and directly compare the ML2P-VAE method to more traditional parameter estimation methods, such as Monte Carlo expectation-maximization.

Author/Presenter

Geoffrey Converse

Mariana Curi

Suely Oliveira

Jonathan Templin 

Lead Organization(s)
Year
2021
Short Description

In this work, we extend variational auto encoders (VAE) to estimate item parameters and correlated latent abilities, and directly compare the ML2P-VAE method to more traditional parameter estimation methods, such as Monte Carlo expectation-maximization. The incorporation of a non-identity covariance matrix in a VAE requires a novel VAE architecture, which can be utilized in applications outside of education.