Technology Enhanced Scaling of Large-Scale Blended Learning Courses

This thesis targets challenges of large-scale teaching at the level of higher education, especially individualization of learning and feedback at large-scale. The challenges addressed are to scale and develop tool enhanced teaching methodologies that work in large cohorts of students (more than 500). To meet these challenges tools were developed which help lecturers to scale the teaching by either providing automated feedback to developers of learning materials (E.Tutorial) or by taking care of the micromanaged certain teaching methodologies require at large-scale (PELE). The foundation of all approaches in this thesis is the collection and analysis of educational data (learning analytics). To be able to collect these educational data at large-scale in different learning environments an open data collection and storage system was developed, which focuses on ensuring students’ privacy. Based on the collected data a system was developed which helps lecturers to develop and improve distance learning materials, by providing lecturers with automatic data analysis about the detailed usage of the learning materials. In this way helping lecturers to understand how the students use the provided distance learning materials without disturbing the process or having access to the students. To further improve the learning of students in large-scale blended learning courses a teaching methodology was implemented based on regular face-to-face feedback discussions over the period of a semester. The aim was to focus on individual students and provide them with high-quality feedback. To implement such a process at large-scale a tool was developed which took care of the required micromanagement to organize the face-to-face feedback discussions and help lecturers to have an overview and stay in control of the whole process. Allowing to scale this process to more than 800 students and 50 teaching assistants, without a drop in quality. The tool enhanced teaching at large-scale and helped to improve the learning process by increasing the efficacy and enabling lecturers to make data-driven decisions. Thereby enabling lecturers to focus on important aspects of teaching instead of spending time on micromanagement or data collection and analysis. The tool enhanced teaching elaborated in this thesis may be deployed to a wide range of courses, where large cohorts of students are taught in a blended learning setting