Multi-faceted Index driven Navigation for Educational Videos in Mobile Phones

Kumar, Abhishek; Srivastava, Kushal; Yadav, Kuldeep; Deshmukh, Om

doi:10.1145/3025171.3025221

Cited by 4 publications

(8 citation statements)

References 15 publications

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“…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%

“…Regarding tasks and technologies, we identified some low-level tasks performed as basic steps, of which, many rely on deep learning approaches. For example, for textual video elements, several studies employed optical character recognition [14,21,29,32,49,50,54,64,67,102,109,114,123,138,152,160,161,179,195,255,261,262,265,273,276], keyword extraction [14,40,43,61,92,105,106,109,112,121,128,131,161,206,255,264,271], generic natural language processing methods (e.g., [29,127,128,194,243]), or utilized word embeddings (e.g., [54,…”

Section: Audio Featuresmentioning

confidence: 99%

“…Keyword navigation: Another frequent kind of proposal for navigation extracted keywords and indexed them according to specific segments of the video [112,121,128,131,182,273].…”

Section: Navigation and Segmentation Toolsmentioning

confidence: 99%

“…Navigation using visual elements: This kind of approach enabled exploration by presenting visual parts of the video, for example, main slides, figures, tables, charts, etc., which the viewer clicked and then jumped to the corresponding segment in the video [131,158,264,273,274].…”

Section: Navigation and Segmentation Toolsmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Audio Featuresmentioning

confidence: 99%

Section: Audio Featuresmentioning

confidence: 99%

“…Keyword navigation: Another frequent kind of proposal for navigation extracted keywords and indexed them according to specific segments of the video [112,121,128,131,182,273].…”

Section: Navigation and Segmentation Toolsmentioning

confidence: 99%

Section: Navigation and Segmentation Toolsmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…The videos were segmented based on semantic cues derived from the video content to accord granular navigation for users to locate the content of interest effectively. Leveraging the multi-modal nature of videos, a multi-faceted index comprising concepts covered in the video, a summary of key-phrases in the video and visual information in the video to serve as navigational cues (Kumar, Srivastava, Yadav, & Deshmukh, 2017). Focusing on YouTube based educational videos, the researchers found that this enhanced the navigation time when used in mobile devices.…”

Section: Resource Consumptionmentioning

confidence: 99%

Design and evaluation of a video retrieval environment for exploratory searches in a learning context

Loke¹

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Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays

Lu,

Xu,

et al. 2023

2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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Multi-faceted Index driven Navigation for Educational Videos in Mobile Phones

Cited by 4 publications

References 15 publications

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

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

Design and evaluation of a video retrieval environment for exploratory searches in a learning context

Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays

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