Personalized Indexing of Attention in Lectures -- Requirements and Concept

Abstract

Web lectures can be employed in a variety of didactic scenarios ranging from add-on for a live lecture to stand-alone learning content. In all of these scenarios, though less in the stand-alone one, indexing and navigation are crucial for real world usability. As a consequence, many approaches like slide based indexing, transcript based indexing, collaborative manual indexing as well as individual or social indexing based on viewing behavior have been devised. The approach proposed in this paper takes individual indexing based on viewing behavior two steps further in that (a) indexes the recording at production time in the lecture hall and (b) actively analyzes the students attention focus instead of passively recording viewing time as done in conventional footprinting. In order to track student attention during the lecture, recoding and analyzing the student's behaviour in parallel to the lecture as well as synchronizing both data streams is necessary. This paper discusses the architecture required for personalized attention based indexing, possible problems and strategies to tackle them.