Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing…

Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…

Purpose: The purpose of this paper is to propose and demonstrate an evidence-based scenario design framework for assessment-based computer games. Design/methodology/approach: The evidence-based scenario design framework is presented and demonstrated by using BELLA, a new assessment-based gaming environment aimed at supporting student learning of…

Sophisticated software such as Virtual Learning Environments (VLEs) are rapidly being deployed by universities. Despite widespread use of such systems, experience shows that there is frequently poor pedagogic development, leading primarily to use of VLEs as electronic document repositories rather than as online learning systems in which the…