Social navigation in web lectures

Mertens, Robert; Farzan, Rosta; Brusilovsky, Peter

doi:10.1145/1149941.1149950

Cited by 30 publications

(29 citation statements)

References 13 publications

(11 reference statements)

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“…We take this data further to build new interaction techniques for video navigation, which prior work has not done. Also, we combine both implicit user history data and explicit user bookmarks to support diverse learning tasks, which extends prior work on supporting social navigation for lecture videos [28].…”

Section: Leveraging Interaction Historymentioning

confidence: 86%

Data-driven interaction techniques for improving navigation of educational videos

Kim

Guo

Cai

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

135

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With an unprecedented scale of learners watching educational videos on online platforms such as MOOCs and YouTube, there is an opportunity to incorporate data generated from their interactions into the design of novel video interaction techniques. Interaction data has the potential to help not only instructors to improve their videos, but also to enrich the learning experience of educational video watchers. This paper explores the design space of data-driven interaction techniques for educational video navigation. We introduce a set of techniques that augment existing video interface widgets, including: a 2D video timeline with an embedded visualization of collective navigation traces; dynamic and non-linear timeline scrubbing; data-enhanced transcript search and keyword summary; automatic display of relevant still frames next to the video; and a visual summary representing points with high learner activity. To evaluate the feasibility of the techniques, we ran a laboratory user study with simulated learning tasks. Participants rated watching lecture videos with interaction data to be efficient and useful in completing the tasks. However, no significant differences were found in task performance, suggesting that interaction data may not always align with moment-by-moment information needs during the tasks.

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Section: Leveraging Interaction Historymentioning

confidence: 86%

Data-driven interaction techniques for improving navigation of educational videos

Kim

Guo

Cai

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

135

View full text Add to dashboard Cite

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“…The user can switch to the history shown in Figure 6 by clicking on the 'History' button: each record is visualized as a video segment with a size indicating how often that interval was viewed. Previous work applied varying colour intensities to indicate the importance of each segment [13]. We used size since we are representing intervals using thumbnails where colour discrimination would be confused with the thumbnail content and would be difficult to differentiate for some videos.…”

Section: Video Navigation Interfacementioning

confidence: 99%

Visualization of personal history for video navigation

Al-Hajri

Miller

Fong

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Figure 1. Our prototype history-based interface called the Video History System (VHS) aids navigation through the management of a user's personal viewing history. Playback of video is controlled with familiar tools such as play/pause, seek and filmstrip (left) -the VHS records each part of the video viewed by the user. The history is then visualized in one of two ways: as Video Tiles (centre) or as a Video Timeline (right). 1 ABSTRACTWe present an investigation of two different visualizations of video history: Video Timeline and Video Tiles. Video Timeline extends the commonly employed list-based visualization for navigation history by applying size to indicate heuristics and occupying the full screen with a two-sided timeline. Video Tiles visualizes history items in a grid-based layout by following pre-defined templates based on items' heuristics and ordering, utilizing screen space more effectively at the expense of a clearer temporal location. The visualizations are compared against the state-of-the-art method (a filmstrip-based visualization), with ten participants tasked with sharing their previously-seen affective intervals. Our study shows that our visualizations are perceived as intuitive and both outperform and are strongly preferred to the current method. Based on these results, Video Timeline and Video Tiles provide an effective addition to video viewers to help manage the growing quantity of video. They provide users with insight into their navigation patterns, allowing them to quickly find previouslyseen intervals, leading to efficient clip sharing, simpler authoring and video summarization.

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“…This provides users with a quick navigation method to similar scenes. Mertens et al [20] visualized users' footprints on the video timeline, which allowed users to quickly navigate to the corresponding scenes in the video. In our interface, we used VCR like controls along with thumbnails of the previously viewed video segments that allow simple navigation to these intervals.…”

Section: Motivationmentioning

confidence: 99%

Video Navigation with a Personal Viewing History

Al-Hajri¹,

Miller²,

Fels³

et al. 2013

Human-Computer Interaction – INTERACT 2013

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Abstract. We describe a new video interface based on a recorded personal navigation history which provides simple mechanisms to quickly find and watch previously viewed intervals, highlight segments of video the user found interesting and support other video tasks such as crowd-sourced video popularity measures and consumer-level video editing. Our novel history interface lets users find previously viewed intervals more quickly and provides a more enjoyable video navigation experience, as demonstrated by the study we performed. The user study tasked participants with viewing a pre-defined history of a subset of the video and answering questions about the video content: 83.9% of questions (average) were answered correctly using the personal navigation history, while 65.5% were answered using the state-of-art method; they took significantly less time to answer a question using our method. The full video navigation interface received an 82% average QUIS rating. The results show that our history interface can be an effective part of video players and browsers. Keywords: Video Navigation; Navigation History; Video Summarization; IntroductionThe dramatic increase in the quantity of video now available, either in the online form of services such as YouTube TM , Vimeo TM or the more personal form of home video, provides new challenges such as finding specific videos (or intervals within video), authoring new videos from existing content (e.g. video summarization or home movie editing), sharing video playlists (or even intervals) of online video and annotating dynamic content. The increased volume and shrinking average duration of online video has led to new use cases: users are generally free to skip ahead to find intervals which interest them; users may re-watch part of the video they enjoyed; users may employ temporal video links to provide instant access to a specific time within a video, skipping irrelevant sections; playlists and temporal links provide opportunities for customization and sharing of video consumption among users. In short, users can and want to view personally interesting intervals easily and without viewing the entire video. These use cases are supported within current interfaces, however there are no existing mechanisms to support new use cases which arise as a natural consequence, such as navigation of previously viewed intervals. Additionally, users often re-watch segments of video as part of the contemporary browsing and navigation experience [24,25,28], and may wish to share these intervals with others. A historical record of video viewing would allow users to quickly find popular intervals, comment on and share them. We introduce our novel video interface, which captures the user's viewing history (analogous to a web browser history) and allows them to reuse it as a navigation tool, through a user-time visualization with supporting interactions. The history supports actions such as faster access to previously viewed intervals, visualization of popular intervals within the video based on a use...

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Social navigation in web lectures

Cited by 30 publications

References 13 publications

Data-driven interaction techniques for improving navigation of educational videos

Data-driven interaction techniques for improving navigation of educational videos

Visualization of personal history for video navigation

Video Navigation with a Personal Viewing History

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