Medical students are taught the importance of variable relationships in scientific research. We propose augmenting this education with teaching about direct acyclic graphs (DAGs)--a graphical model used to analyze complex systems of variables. DAGs are increasingly used in medical research and education about their creation and use would be…

Binding price controls (i.e., price floors and ceilings) hinder the efficiency of markets by eliminating mutually agreeable transactions, which are often graphically illustrated in principles of economics textbooks using Harberger's Triangle. However, price controls create another form of inefficiency by eliminating the incentives for the…

Single-case research design is a useful methodology to investigate counseling treatment effects through a time-series graph. A systematic review of 42 counseling journals was conducted, yielding 50 studies, including 272 graphs. Most graphs did not meet recommended guidelines. How graphs impact visual analysis and suggestions to enhance…

Intelligent technology plays a pivotal role in revolutionizing learning assessments, overcoming the constraints of traditional assessment methods and driving educational innovation. Knowledge tracing (KT) emerges as a critical component for assessing students' learning states and forecasting their future performance. However, existing graph-based…

This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its…

This article examines newly introduced eccentricity topological descriptors that are used to analyze the structures of polycyclic aromatic hydrocarbons. The purpose is to identify structural components that have a significant impact on physicochemical properties. Regression analysis applies to find potential correlation between different drugs…

Knowledge tracing (KT) plays a key role in adaptive learning, yet traditional recurrent neural network-based methods often struggle with sparse data and overlook relationships between knowledge points. To address these limitations, this paper proposes a novel knowledge tracing model (KGS-KT) that integrates knowledge graphs, graph convolutional…

While previous educational governance literature on datafication has paid attention to comparison across spatial entities like countries and schools, temporal comparison in terms of progression (including prediction) has received less attention. One of the material forms in which progression and prediction data are circulated is the visual form of…

This study addressed an issue relevant to information and research literacy: how students cope with the challenges of reading a one-sided informational text that is combined with ambiguous graphical information. The topic discussed in the text was the impact of digitalization on students' reading comprehension, and 120 university students were…

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as…

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of [tau], the between-study standard deviation, and the shrunken estimates of the study effects as a…

Computational learning theory studies the design and analysis of learning algorithms, and it is integral to the foundation of machine learning. In the modern era, classical computational learning theory is growingly unable to catch up with new practical demands. In particular, problems arise in the following aspects: i). "scalability":…

The aim of this paper is to explore the use of function graphs to model a given picture of the Golden Gate Bridge, an iconic architectural structure located in San Francisco. This illustration is sourced from the guide on drawing the Golden Gate Bridge ("How to Draw the Golden Gate Bridge", n.d.). We use the digital graphing calculator…

Item difficulty and dimensionality often correlate, implying that unidimensional IRT approximations to multidimensional data (i.e., reference composites) can take a curvilinear form in the multidimensional space. Although this issue has been previously discussed in the context of vertical scaling applications, we illustrate how such a phenomenon…

Visual analysis is the primary methodology used to determine treatment effects from graphed single-case design data. Previous studies have demonstrated mixed findings related to interrater agreement between both expert and novice visual analysts, which represents a critical limitation of visual analysis and supports calls for also presenting…