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The e-learning community has been producing and using video content for a long time, and in the last years, the advent of MOOCs greatly relied on video recordings of teacher courses. Video annotations are information pieces that can be anchored in the temporality of the video so as to sustain various processes ranging from active reading to rich media editing. In this position paper we study how video annotations can be used in an e-learning context -especially MOOCs -from the triple point of view of pedagogical processes, current technical platforms functionalities, and current challenges. Our analysis is that there is still plenty of room for leveraging video annotations in MOOCs beyond simple active reading, namely live annotation, performance annotation and annotation for assignment; and that new developments are needed to accompany this evolution.

In this work, we propose a learning analytics implementation based on a model-driven engineering approach. It aims at assessing the benefits that could arise from such an implementation, when pedagogical resources are produced via publishing chains, that already use the same approach to produce documents. Previously, we have discussed these potential benefits from a more theoretical point of view. In the present work, we present a concrete implementation of a metamodel to integrate a learning analytics system closely linked to the knowledge of the semantics and structure of any document produced, natively. Finally, we present an initial evaluation of this metamodel by modelers and discuss the limits of this metamodel and the future changes required.

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