My interest in Learning Analytics is primarily focused on building novel learning analytics methods for assessing the learning processes in networked learning settings. The central topic of networked learning research are human-human and human-information interactions that occur within a networked learning environment. Given that the nature of these interactions is highly complex, a multi-dimensional approach is usually required in analyzing their effects. Therefore, the main goal of my research is the development of a theoretical model that allows for a comprehensive and scalable analysis of learners' engagement into collaboration in networked communities.
The anticipated outcomes of the proposed research will advance understanding of learning and teaching processes in networked learning, thus contributing to the existing research and practice of digital learning and teaching. Having an effective model that might explain learning processes within networked learning communities in a timely and reliable manner might significantly improve learning and help answering to individual needs of every learner.
The project will extend, apply, and empirically validate new methods of learning analytics that useintensively sampled, fine-grained, temporally ordered data about learning activities to model learning,self-regulated learning (SRL), and motivation as dynamic processes. My current work is related todiscourse analysis, process mining, concept maps extraction and application of natural languageprocessing tools and technologies.
This is industrial collaborative research project with Desire2Learn, leading vendor in the technologyenhanced learning market. The main objectives of the project are analytics of learning quality insocial learning environments and concept comprehension in social learning. Major outcome of thisproject is development of open-source, semi-automatic software for content analysis of students’transcripts.