Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Equating is a statistical procedure used to adjust for the difference in form difficulty such that scores on those forms can be used and interpreted comparably. In practice, however, equating methods are often implemented without considering the extent to which two forms differ in difficulty. The study aims to examine the effect of the magnitude…

Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Entrepreneurship education has evolved to meet the demands of a dynamic business environment, necessitating innovative teaching methods to prepare entrepreneurs for market uncertainties. Large Language Models (LLMs) like the Generative Pre-trained Transformer 4 (GPT-4), recognized for their exceptional performance on public datasets, are examined…

Many students persistently misinterpret histograms. This calls for closer inspection of students' strategies when interpreting histograms and case-value plots (which look similar but are different). Using students' gaze data, we ask: "How and how well do upper secondary pre-university school students estimate and compare arithmetic means of…

Flame Emission Spectroscopy (FES) is a powerful analytical technique widely used for identifying and quantifying elements in various samples. In this laboratory experiment, undergraduate students were introduced to FES and its significance in analytical chemistry. The experiment aimed to provide students with hands-on experience in constructing…

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which…

The statistical properties of data are not present in any individual value, but rather, emerge only by perceiving the set as a whole. Summarizing the statistical properties of sets (e.g., creating ensembles) is ubiquitous in cognition, yet one unanswered question is how this process changes over development. The properties of number sets (e.g.,…

Health risks, when presented as ratios (e.g., two out of seven people), are challenging to understand, but visual displays can foster accurate understanding. We conducted three experiments to test how characteristics of numbers (Experiment 1), icon arrays (Experiments 1, 2, and 3), and number lines (Experiments 1 and 3) influenced people's ability…

The COVID-19 pandemic has necessitated the need to reliably detect the presence of viral genomes in human clinical samples. The most accurate viral tests involve the use of qPCR. Thus, it is important for students to understand the mechanism to detect viral genomes by qPCR including critical qPCR controls and how to properly interpret qPCR data.…