| Περίληψη: | The thesis deals with ways to improve and maintain the instructional design quality in Massive Open Online Courses (MOOCs). More concretely, our goal is to support the decision making of MOOC practitioners (e.g., instructional designers, instructors) at run-time, i.e., when the course is active, on instructional quality issues. The main gap that we address is that existing evaluation studies of instructional quality do not assess the supporting strategies adopted by the instructional staff (i.e., learner facilitators) to support learners in the forum. To address this gap we follow a three-phase research methodology. In the first phase we perform three case studies that extend the existing empirical research on the intervention characteristics and the problems of learner facilitators in courses of different subject matter. In the second phase, we perform an in-depth case study in which we provide empirical evidence about the main instructional approaches learner facilitators adopt in MOOC forums, and unravel the quality issues that are associated with them. Then, we proceed to the formulation of a data-driven methodology that aims to guide MOOC system-tool developers in designing tools that help instructors keep track of such instructional approaches, and support their decision-making at run-time. In the last phase, we use the data-driven methodology to design and develop a runtime tool that supports the decision making of instructors in MOOCs. Specifically, we provide a proof of concept regarding the feasibility and usefulness of the proposed methodology in designing new software applications. This contribution has been evaluated throughout the research process of three case studies performed in MOOC contexts, and a set of semi-structured interviews with experts.
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