Semantic Open Courseware

Fields: Knowledge Extraction, Online Education, Recommendation.

Research Focus: This line of research pushes for human-centered information systems in the educational domain, i.e. instead of simply offering learning materials with limited meta-data, content is analyzed with respect to which parts human users consider relevant for a certain information need. Thus, this is another application of my fundamental theory to domain-specific challenges. From a technology point of view, this shares many similarities with the previous of line of work of entity-centric extraction in digital libraries, as many methods, insights, and results can be shared across both research lines. Ultimately, I envision that both research lines can even be unified into a shared prototype implementation which can also bridge and integrate scientific knowledge into university education tailored to individual users/students.

Domain-Specific Pitch: Online education has seen a tremendous growth during the recent years, covering e-learning offers ranging from traditional online courses to Massive Open Online Courses (MOOCs) and private online courses (SPOCs). Additionally, nearly all higher education institutions support their on-site courses by providing the necessary materials like slides or other course material digitally. At the heart of this development are courseware infrastructures, platforms handling the communication between learners and teachers, and storing and distributing all relevant learning objects. Those learning objects, usually created, curated, and tailored with great care and costs by educational or domain experts represent a significant investment. Therefore, it has been a long-term challenge to provide these learning objects as open education resources to a wide public to maximize their impact with the goal to support a wide variance of target audiences like traditional learners in university courses or online courses, professionals which need to obtain focused competencies, but also educators at higher educational institutions to motivate the reuse of high-quality material to free up valuable personal resources. However, while nowadays there a multitude of platforms offering whole courses with varying degree of openness, this vision of easy access to fine-grained open educational resources has still not been fully realized. One reason for this is that current courseware platforms lack semantic and analytic capabilities to support the sophisticated query, search, and recommendation requirements necessary to efficiently serve the specific information needs of individual learners or educators – courseware platforms are mostly used as repositories for storing and statically serving learning resources alongside manually created meta-data with respect to fixed learning paths as provided by the course designer. The goal of this project is to complement current courseware platforms with state-of-the-art semantic analysis capabilities to obtain deep understanding of both the users and the resources stored in a courseware platform to provide personalized access tailored to the individual information need of users. At the core of my suggested solution are micro-learning objects, i.e. the smallest units of thematic cohesion found in learning content. In this project, I aim at identifying, extracting, and semantically annotation such micro-learning objects in an automated fashion. This annotation will cover multiple facets perceived relevant by both learners and educations like topics, didactic intend, required expertise, or perceived attributes (i.e. based on user judgements).

Christoph Lofi