Augmenting Video Lectures: Identifying Off-topic Concepts and Linking to Relevant Video Lecture Segments

Ghosh, Krishnendu; Nangi, Sharmila Reddy; Kanchugantla, Yashasvi; Rayapati, Pavan Gopal; Bhowmick, Plaban Kumar; Goyal, Pawan

doi:10.1007/s40593-021-00257-z

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…Tools, tasks and technologies: The main tools, pipelines, frameworks, and methods that have been proposed for supporting VBL were related to information extraction, integration of interactive features, recommendation systems, classification and prediction, video navigation, and summarization (see Section 5.1 Figure 5). Information extraction focused mainly on retrieving text [49,49,50,72,72,115,115,115,123,123,124,138,138,262,262,276,276], metadata [61,64,77,109,111,176], and key information from videos such as key video segments and key topics [32,76,128,148,152,195]. Some approaches used deep learning for information extraction [32,49,115,148], but also other (shallow) machine learning methods based on hand-crafted features were still present [32,152,195].…”

Section: Discussionmentioning

confidence: 99%

“…Some approaches used deep learning for information extraction [32,49,115,148], but also other (shallow) machine learning methods based on hand-crafted features were still present [32,152,195]. Knowledge bases [77,109,176] and natural language [61,64,111] processing methods were also utilized.…”

Section: Discussionmentioning

confidence: 99%

“…Furini [64] extracted tags by measuring the most frequent words. Ghosh et al [77] detected concepts that are not familiar or do not belong to the domain of the video content but could be relevant to better understand the video. The concepts were linked to Wikipedia articles, which were used to build a concept similarity network.…”

Section: Information Extraction Toolsmentioning

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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Information Extraction Toolsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Finally, the paper links the off-topic concepts to relevant video segments. Overall, the paper's approach involves segmenting the video lecture into smaller parts, extracting text from each segment, identifying the topics covered in each segment, identifying off-topic concepts based on the identified topics, and linking off-topic concepts to relevant video segments [3].…”

Section: Literature Surveymentioning

confidence: 99%

Video Transcript Summarization Using Bert

Iswarya¹,

Krishna²,

Naveen³

et al. 2023

Int. J. Res. Publ. Rev.

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Throughout the years, the Internet and social networking sites have experienced a significant expansion in multimedia, notably video material. The amount of video data created has expanded at an exponential pace over time. Yet, many people are unable to read the entire information owing to time limits. The goal of video summarization is to create video summaries that incorporate the most significant and relevant material from a video stream in a concise manner. The summarising technique can remove a significant quantity of information while maintaining the most important information in the article. Our model has received the video, and the speech will be retrieved. The model then translates audio to text by chunking an audio file, which is handy for processing big files. The voice is then automatically transformed into text using automated speech recognition, which is a quick and simple approach to work with audio files. It transforms audio clips to text. Lastly, the BERT algorithm is applied to text summarization. Transformer, an attention mechanism that learns the contextual relationships between words in a summarised text, is used in the summarising process.

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Technological Acceleration in Business Education—Study of Educator’s Attitude and Behavior Towards Usage of Technology: UTAUT Framework

Shukla

Prasad

2023

Studies in Systems, Decision and Control

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Augmenting Video Lectures: Identifying Off-topic Concepts and Linking to Relevant Video Lecture Segments

Cited by 5 publications

References 46 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

Video Transcript Summarization Using Bert

Technological Acceleration in Business Education—Study of Educator’s Attitude and Behavior Towards Usage of Technology: UTAUT Framework

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