Educational Process Analysis aims to discover latent learning and teaching processes that are hidden within temporal educational data. Typically, the process data has information about what students and teachers do over time. From such data, we can discover different representations of the unobserved learning and teaches processes that might be producing the observed data. In other words, the process data can be thought of as resulting from a set of hidden learning and teaching processes occurri
Educational Process Analysis aims to discover latent learning and teaching processes hidden within educational data. Processes hidden in the data can be represented by various types of constructs and models. Techniques such as Association Rule Mining and Graph-Based Analysis can help us discover different representations of educational processes. Process data can come in many different shapes and sizes, and we have to use different methods for different types of data. Process Models and Graph-Based Analysis can give an end-to-end view of the student interaction data as process models or graphs. Curriculum Pacing produces a clear visualization of how students follow the curriculum over time.