Over the past few years, with the rapid adoption of broadband communication and advances in multimedia content capture and delivery, web-based meetings and lectures, also referred to as emeeting and e-lecture, have become popular among businesses and academic institutions because of their cost savings and capabilities in providing self-paced education and convenient content access and retrieval. In fact, the technological achievements in capture, analysis, access, and delivery of emeeting and e-lecture media have already resulted in several working systems that are currently of regular usage. This paper gives an overview of existing work as well as state-of-the-art in these two research areas which are bound to affect the way we teach, learn, and collaborate. 1.

We introduce new techniques for extracting, analyzing, and visualizing textual contents from instructional videos of low production quality. Using Automatic Speech Recognition, approximate transcripts (≈75 % Word Error Rate) are obtained from the originally highly compressed videos of university courses, each comprising between 10 to 30 lectures. Text material in the form of books or papers that accompany the course are then used to filter meaningful phrases from the seemingly incoherent transcripts. The resulting index into the transcripts is tied together and visualized in 3 experimental graphs that help in understanding the overall course structure and provide a tool for localizing certain topics for indexing. We specifically discuss a Transcript Index Map, which graphically lays out key phrases for a course, a Textbook Chapter to Transcript Match, and finally a Lecture Transcript Similarity graph, which clusters semantically similar lectures. We test our methods and tools on 7 full courses with 230 hours of video and 273 transcripts. We are able to extract up to 98 unique key terms for a given transcript and up to 347 unique key terms for an entire course. The accuracy of the Textbook Chapter to Transcript Match exceeds 70 % on average. The methods used can be applied to genres of video in which there are recurrent thematic words (news, sports, meetings, …)

The purpose of the study was to investigate the impact of an experimental online learning tool on student performance. By applying cognitive load theory to online learning, the experimental tool used was designed to minimize cognitive load during the instructional and learning process. This tool enabled students to work with programming code that was supplemented with instructor descriptions and feedback, embedded directly within the code while maintaining the original integrity of the coding environment. A sample of 24 online graduate students at a southeastern university were randomly assigned to four groups: Group 1 (Control group), Group 2 (Assessment group: the tool was used to provide feedback on student work),

Videos are rich in multimedia content and semantics, which should be used by video browsers to better present the audio-visual information to the viewer. Ubiquitous video players allow for content to be scanned linearly, rarely providing summaries or methods for searching. Through analysis of audio and video tracks, it is possible to extract text transcripts from audio, displayed text from video, and higher-level semantics through speaker identification and scene analysis. External data sources, when available, can be used to cross-reference the video content and impose a structure for organization. Various research tools have addressed video summarization and browsing using one or more of these modalities; however, most of them assume edited videos as input. We focus our research on genres in personal interaction videos and collections of such videos in their unedited form. We present and verify formal models for their structure, and develop methods for their automatic analysis, summarization and indexing. We specify the characteristic semantic components of three related genres of candidly captured videos: formal instructions or lectures, student team project presentations, and discussions. For each genre, we design and

Lecture videos captured in classrooms are an integrated part of e-learning. Effective learning with these videos remains a challenging task. We present VAM (Video and More), a tool which automatically creates hierarchical structured video sequences for e-learning and distance learning purposes. The innovative approach of this system is the automatic extraction of structure and annotations for later use as navigation support. The structural information is generated from the PowerPoint-slides used in the recorded presentation and is utilized to provide the learner with an elaborated, combined and enriched view of the material. The information for segmentation and synchronization of video, audio and slides are automatically stored during the recording session. The user can navigate either by choosing topics, slides, time or importance of different parts of the multimedia-presentation. The audio and video information is combined with hyperlinks and annotations of the presenter. Additionally the users can add and distribute their own annotations which are temporally bound to specific parts of the video. In this way the video sequences are not only randomly accessible and the important parts easily recognizable but they are enriched with additional information and the possibility of