Automatic Curation of Content Tables for Educational Videos

Mukherjee, Arpan; Tiwari, Shubhi; Chowdhury, Tanya; Chakraborty, Tanmoy

doi:10.1145/3331184.3331400

Cited by 6 publications

(6 citation statements)

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“…Moderately studied characteristics were related to audio features, production style, and instructor behavior. Audio features specifically have been used to develop tools [14,21,31,35,64,64,65,71,76,81,102,103,109,112,112,131,148,160,161,174,182,202,207,220,220,250,273]. Different types of production style have been tested mainly in controlled experiments [6,30,42,49,55,93,136,180,181,190,191,223,224,229,230] (see Figure 3).…”

Section: Discussionmentioning

confidence: 99%

“…Navigation tools popularly proposed table content navigation styles [21,48,71,102,128,131,160,174,264,273] but some interesting approaches have emerged such as navigation according to keywords [112,121,128,131,182,273] or visual elements [131,158,264,273,274]. These tools have relied heavily in technologies to segment the video and to extract subtopics, concepts, and keywords.…”

Section: Discussionmentioning

confidence: 99%

“…Audio records [14,21,31,64,65,71,81,102,109,112,131,148,160,161,182,202,207,211,220,250,273] Sound-related [35,56,64,76,103,112,174,211,212,214,220] Energy, entropy, spectral, jitter, shimmer, noise, Mel-frequency cepstral coefficients, perceived quality Representation characteristics (cues) [211,253] Instructor-related: verbal cues Visual features Video frames and images (figures and illustrations) [7, 14, 21, 29, 32, 38, 49, 50, 64, 65, 67, 72, 76, 80, 81, 83, 83, 102, 111, 111, 114, 115, 121, 123, 124, 131, 138, 148, 153, 158, 160, 161, 179, 182, 195, 198, 198, 207, 252, 255, 261-264, 264, 265, 273, 274, 274, 276] Representation characteristics (cues) [110,160,170,192,193,209,209,251,253] Instructor-related: pointing gestures, directed gaze. Content-related: highl...…”

Section: Audio Featuresmentioning

confidence: 99%

“…Video segmentation: One of the natural ways of splitting a video is to detect slide changes, which can be used to extract key video frames. For video segmentation, TextTiling [174,273] and dynamic programming [71] were suggested.…”

Section: Navigation and Segmentation Toolsmentioning

confidence: 99%

“…or keyword is presented. The segments to which the sub-topic or keyword belong are identified and indexed to allow the learner to click on them and to jump there directly[21,48,71,102,128,131,160,174,264,273]. Sub-topics and keywords have been extracted with technologies such as the Latent Dirichlet Allocation algorithm[21,102,128,174],…”

mentioning

confidence: 99%

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A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

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People increasingly use videos on the Web as a source for learning, be it for daily tasks or in formal as well as informal educational settings. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning from computer science databases for the period from 2016 to 2021 using the PRISMA guidelines. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners' activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner's activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner's activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends in the development of design guidelines to produce learning videos.

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