Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…

A design experiment where students in Grade 5 (11-12 years old) play the Color Run game constitutes the context for investigating how students can be introduced to informal hypothesis testing. The result outlines a three-step hypothetical learning trajectory on informal hypothesis testing. In the first step, students came to favor sample space…

In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good…

The typical collegiate introductory statistics course poses significant challenges for students. Many do not fully comprehend key course skills, and it is common for students to exit the class with a neutral or negative attitude toward statistics. To measure the impact of using relevant contextual examples as an instructional strategy during a…

This study investigated dimensionality of Binary Response Items through a non-parametric technique of Item Response Theory measurement framework. The study used causal comparative research type of nonexperimental design. The sample consisted of 5,076 public senior secondary school examinees (SSS3) between the age of 14-16 years from 45 schools,…

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as…

Some situations are presented with perplexing properties, which become clearer by looking at contingency tables. This in turn leads to problems that can be solved using conditional frequencies and thus leading to the Bayes formula with natural frequencies or probabilities.

In this article we propose the use of Bayesian networks as a potentially promising way to model usable knowledge. Using the Classroom Video Analysis (CVA and CVA-M) assessments as a lab model for studying teachers' usable knowledge, we first explored whether we can identify the knowledge (pieces) underlying teachers' written responses. In the CVA…

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for…

Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty. This research aims at Bayesian network structure learning and how the learned model can be used for reasoning. Learning the structure of Bayesian network from data is…

School district consolidation has continued at a rapid pace in the United States, with one in every nine districts consolidating between 2000 and 2015 (Collins forthcoming). The stated aim of consolidation efforts is usually improved efficiency -- growing larger to lower unit costs or improve student outcomes without spending more money (Callahan…

M-fluctuation tests are a recently proposed method for detecting differential item functioning in Rasch models. This article discusses a generalization of this method to two additional item response theory models: the two-parametric logistic model and the three-parametric logistic model with a common guessing parameter. The Type I error rate and…

Student-generated questions are an important mechanism for learning and self-regulation, yet their scarcity in classroom discourse points to a need to understand how students decide to ask or withhold their questions. This study examined how 12 graduate students in an introductory statistics course made decisions about asking questions during…

Statistics is interdisciplinary and the practical application of statistical methods in various areas prompts undergraduates to learn more about statistics and better understand complex methods. This article presents a classroom teaching design that guides students in reading COVID-19 literature. The activities presented encourage peer-peer and…

The profound advancements in technology have rendered novel forms of data and data visualisation increasingly accessible to individuals within society, thereby influencing daily decision-making processes. To address this change, this study sets out to review recent research on data-driven inquiries at the K-12 level from two perspectives:…