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…

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…

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…

Several programs of research have sought to assess the replicability of scientific findings in different fields, including economics and psychology. These programs attempt to replicate several findings and use the results to say something about large-scale patterns of replicability in a field. However, little work has been done to understand the…

Although much research has been conducted on the psychometric properties of cognitive diagnostic models, they are only recently being used in operational settings to provide results to examinees and other stakeholders. Using this newer class of models in practice comes with a fresh challenge for diagnostic assessment developers: effectively…

Heterogeneity in response styles can affect the conclusions drawn from rating scale data. In particular, biased estimates can be expected if one ignores a tendency to middle categories or to extreme categories. An adjacent categories model is proposed that simultaneously models the content-related effects and the heterogeneity in response styles.…

Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to…

Simulation games can play a critical role in enabling students to navigate the problem spaces of the past while simultaneously critiquing the models designers offer to represent those problem spaces. There is much to be gained through their use. This includes rich opportunities for students to engage the past as independent historians; to consider…