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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).

Examining teachers’ knowledge on a large scale involves addressing substantial measurement and logistical issues; thus, existing teacher knowledge assessments have mainly consisted of selected-response items because of their ease of scoring. Although open-ended responses could capture a more complex understanding of and provide further insights into teachers’ thinking, scoring these responses is expensive and time consuming, which limits their use in large-scale studies.

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.

Diagnostic classification models (DCMs) are psychometric models for evaluating a student’s mastery of the essential skills in a content domain based upon their responses to a set of test items. Currently, diagnostic model and/or Q-matrix misspecification is a known problem with limited avenues for remediation. To address this problem, this paper defines a one-sided score statistic that is a computationally efficient method for detecting under-specification at the item level of both the Q-matrix and the model parameters of the particular DCM chosen in an analysis.

This book is about scientific inquiry. Designed for early and mid-career researchers, it is a practical manual for conducting and communicating high-quality research in (mathematics) education. Based on the authors’ extensive experience as researchers, as mentors, and as members of the editorial team for the Journal for Research in Mathematics Education (JRME), this book directly speaks to researchers and their communities about each phase of the process for conceptualizing, conducting, and communicating high-quality research in (mathematics) education.

As distinct communities of practice (COP), science education research (SER) and environmental education research (EER) have both matured a great deal in recent decades, coming to include a greater diversity of theoretical perspectives, worldviews, and researcher and participant voices. In this paper, we present a view of theoretical inclusivity that promises a rich, robust research landscape for both EER and SER through the deliberate inclusion of non-Western theories.

This study investigates the emergence and cultivation of teachers' “epistemic empathy” in response to analyzing videos of student inquiry. We define epistemic empathy as the act of understanding and appreciating someone's cognitive and emotional experience within an epistemic activity—i.e., activity aimed at the construction, communication, and critique of knowledge.

Efforts to promote reform-based instruction have overlooked the import of affect in teacher learning. Drawing on prior work, I argue that teachers’ affective experiences in the discipline are integral to their learning how to teach the discipline. Moreover, I suggest that both affective and epistemological aspects of teachers’ experiences can serve to cultivate their epistemic empathy—the capacity for tuning into and valuing someone’s intellectual and emotional experience within an epistemic activity—in ways that support student-centered instruction.

This toolkit offers education research tips, resources, and tools on research design, theory & frameworks, methodologies, instrumentation, human subjects, and data management.

Research Design