Learning analytics is a young field in computer supported learning, which could have a great impact on education in the future. It is a set of analytical tools which measure, collect, analyze and report about students' data for the purpose of understanding and optimizing students' learning and environments in which this learning occurs.
Today, more and more learning related activities are placed on the web. Teachers are creating virtual learning environments (VLE), in which a great set of data about students is created. This data falls into category of »big data«. If used correctly, this data has great potential for teachers and other educational workers to better understand the process of learning and thus optimize it better and adapt it for their students. To interpret the data from VLEs we use learning analytics. They allow us to choose, collect and process the data, which is the used to extract useful information, bringing us closer to our main goal of improving teachers' way of teaching and students' learning outcomes.
Learning analytics are still in the early phases of development. Although there are already some good results, which indicate that using tools for learning analytics can really improve learning outcomes, learning analytics are still relatively unknown among teachers and other educational workers. This Master’s thesis consequently introduces learning analytics and presents their history and connected concepts. We also describe a reference model for learning analytics and we discuss their phases and components in detail.
One of the main features of learning analytics is their connection with powerful and complex programming tools, which are, at least for now, expensive and therefore unavailable for most schools and teachers. In this Master’s thesis we introduce our own programming tool for learning analytics, POUK. This tool was built with Microsoft Excel and programming language Visual Basic for Applications and is processing the data from virtual learning environment Moodle.
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