We share lessons gained through supporting an institution-wide curriculum innovation via a post-graduate professional learning program. At the inception of the innovation, an intensive Block Model (BM) was unfamiliar to both the institution and its professional learning facilitators. The Graduate Certificate of Tertiary Education was re-modelled…

This paper is concerned with the process of selecting between the increasingly popular bi-factor model and the second-order factor model in measurement research. It is indicated that in certain settings widely used in empirical studies, the second-order model is nested in the bi-factor model and obtained from the latter after imposing appropriate…

Concepts contain rich structures that support flexible semantic cognition. These structures can be characterized by patterns of feature covariation: Certain features tend to cluster in the same items (e.g., "feathers," "wings," "can fly"). Existing computational models demonstrate how this kind of structure can be…

A growing body of evidence has attested to the higher impact of COVID-19 on individuals with intellectual disabilities (IDs) than on members of the general population during the pandemic, mainly showing their higher vulnerability. However, we believe it is important to better understand how their situation interacts with the specific circumstances…

In this paper, we draw on recent research on so-called Fermi problems and situate the fundamental principles underlying this type of tasks and their use from a task design perspective. We use the models and modelling perspective on teaching and learning to elaborate on aspects related to the design of single-use, as well as sequences of, Fermi…

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…

Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This practice allows the teacher to intervene in learning in the first few weeks of class and maximize student gains. However, the biggest…

Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in…

Surface anatomy is an important skill for students in preparation for patient care, and peer examination is often used to teach musculoskeletal and surface anatomy. An alternative pedagogical approach is to use bodies represented in artworks. Represented bodies display fictive anatomy, providing students with the opportunity to apply their…

The open online course (MOOC) platform has seen an increase in usage, and there are a growing number of courses accessible for people to select. An effective method is urgently needed to recommend personalized courses for users. Although the existing course recommendation models consider that users' interests change over time, they often model…

Three quantitative methods are compared for their ability to understand different COVID-19 fatality ratios in 33 OECD countries. Linear regression provides a limited overview without sensitivity to the diversity of cases. Cluster Analysis and Dynamic Patterns Synthesis (DPS) gives scrutiny to the granularity of case similarities and differences,…

Autism has been associated with increased mortality risk among adult populations, but little is known about the mortality risk among children and young people (0-24 years). We used a 15-year nationwide birth cohort study using linked health and non-health administrative data to estimate the mortality risk among Autistic children and young people…

Discrete-time survival analysis (DTSA) models are a popular way of modeling events in the social sciences. However, the analysis of discrete-time survival data is challenged by missing data in one or more covariates. Negative consequences of missing covariate data include efficiency losses and possible bias. A popular approach to circumventing…

This paper is part 4 in a series of workshops that examine the properties of some simple models of vocabulary networks. This Workshop explores how the overall activity level of a vocabulary network can be altered by changing the connections in the network (i.e., by implementing relinking events). The Workshop is linked to an online practice room…

Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a…