Multidimensionality assessment: methods comparison and application

Online learning environments often generate a large amount of log data, that can be supplemented by other data sources (such as video, eye tracking, and physiological data). The thesis will further develop, apply and evaluate statistical techniques and models that can be used to analyze these data sets or to track changing variables on the fly in order to optimize and personalize the environment. Two topics are involved in detail, including the multidimensionality measurement and tracking techniques. The techniques related to multidimensionality measurement can be sorted into exploratory and confirmatory techniques. The thesis will compare these techniques to conclude some recommendations based on different situations. Regarding tracking techniques, various options are also present in the literature and the thesis will further develop them to fulfill the research target of adaptive and personalized learning.