Community learning analytics support for audio-visual web-based learning contents: The CIDRE framework

Abstract

An abundance of audio-visual (AV) learning content is available on the Web, e.g. in massive open online courses (MOOC), in Webinars and on AV streaming platforms. However, the coherence of the AV content is depending on the resources put into the production of the material. This results in a big gap between learning analytics capabilities in MOOcs and those possible in self-regulated learning communities on the Web. In this vision paper, we introduce the CIDRE (Communities of learners InterlinkeD via REssources from linked open data) framework. It aims at narrowing and ultimately bridging the gap between contextualized knowledge and learning communities by a hybrid approach: AV contents will be raised to the entity-level and interconnected withsuitable linked open data for the linked learning communities. Based on the, results, we utilize a community specific learning analytics framework. We will evaluate the approach in real learning communities using stereotypic pedagogical scenarios on the Web.