People increasingly use online video platforms, e.g., YouTube, to locate educational videos to acquire knowledge or skills to meet personal learning needs. However, most of existing video platforms display video search results in generic ranked lists based on relevance to queries. The design of relevance-oriented information display does not take into account the inner structure of the knowledge domain, and may not suit the need of online learners. In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. ConceptGuide features a computational pipeline that performs content analysis on the transcripts of YouTube videos retrieved for a topic, and generates concept-map-based visual recommendations of inter-concept and inter-video links, forming learning pathways as structures for learners to consume. We evaluated ConceptGuide by comparing the design to the general-purpose interface of YouTube in learning experiences and behaviors. ConceptuGuide was found to improve the efficiency of video learning and helped learners explore the knowledge of interest in many constructive ways.

Online learning platforms such as MOOCs have been prevalent sources of self-paced learning to people nowadays. However, the lack of peer accompaniment and social interaction may increase learners' sense of isolation and loneliness. Prior studies have shown the positive effects on visualizing peer students' appearances with virtual avatars or virtualized online learners in VR learning environments. In this work, we propose to build virtual classmates, which were constructed by synthesizing previous learners' messages (time-anchored comments). Configurations of virtual classmates, such as the number of classmates participating in a VR class and the behavioral features of the classmates, can also be adjusted. To build the characteristics of virtual classmates, we propose a technique called comment mapping to aggregate prior online learners' comments to shape virtual classmates' behaviors. We conduct a study with 100 participants to evaluate the effects of the virtual classmates built with and without the comment mapping and the amount of virtual classmates rendered in VR. The findings of our study suggest design implications for developing virtual classmates in VR environments.

Abstract MOOCs (Massive Open Online Courses) are increasingly prevalent as an online educational resource open to everyone and have attracted hundreds of thousands learners enrolling these online courses. At such scale, there is potentially rich information of learners' behaviors embedded in the interactions between learners and videos that may help instructors and content producers adjust the instructions and refine the online courses. However, the lack of tools to visualize information from interactive data, including messages left to the videos at particular timestamps as well as the temporal variations of learners' online participation and perceived experience, has prevented people from gaining more insights from video‐watching logs. In this paper, we focus on extracting and visualizing useful information from time‐anchored comments that learners left to specific time points of the videos when watching them. Timestamps as a kind of metadata of messages can be useful to recover the interactive dynamics of learners occurring around the videos. Therefore, we present a visualization system to analyze and categorize time‐anchored comments based on topics and content types. Our system integrates visualization methods of temporal text data, namely ToPIN and ThemeRiver, which can help people understand the quality and quantity of online learners' feedback and their states of learning. To evaluate the proposed system, we visualized time‐anchored commenting data from two online course videos, and conducted two user studies participated by course instructors and third‐party educational evaluators. The results validate the usefulness of the approach and show how the quantitative and qualitative visualizations can be used to gain interesting insights around learners' online learning behaviors.

Online learning is increasingly prevalent as an option for self-learning and as a resource for instructional design. Prerecorded video is currently the main medium of online education content delivery and instruction; this affords asynchronicity and flexibility, and enables the dissemination of lecture content in a distributed and scalable manner. However, the same properties may impede learners' engagement due to the lack of social interaction and peer support. In this paper, we propose a time-anchored commenting interface to allow online learners who watch the same video clips to exchange comments on them. Comments left by previous learners at specific time points of a video are displayed to new learners when they watch the same video and reach those time points. We investigated how the display of time-anchored comments (dynamic or static) and type of comments (content-related or social-oriented) influenced users' perceived engagement, perceived social interactivity, and learning outcomes. Our results show that dynamically displaying time-anchored comments can indeed enhance learners' perceived social interactivity. Moreover, the content of comments would further affect learners' intention of commenting. Based on our findings, we make various recommendations for the improvement of social interaction and learning experience in online education.