Boocture: Automatic Educational Videos Hierarchical Indexing with eBooks

Horovitz, Shay; Ohayon, Yaniv

doi:10.1109/tale48869.2020.9368461

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Further interesting articles proposed methods to create videos automatically [65,87,88], alleviate the split attention problem [81,209], enhance speech recognition [92,202], and find content that is similar not in other videos but in other media such as in textbooks [252] and books [97].…”

Section: Othermentioning

confidence: 99%

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: Othermentioning

confidence: 99%

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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“…The video is usually divided into small segments (e.g by using voice activity detection [38]) or temporal windows. Then, existing transcripts or automatic speech recognition (ASR) are used to create an embedding for each segment based on methods like bags of words [39], word2vec [38]- [40], and TF-IDF [41]. Some methods also consider additional acoustic features [38], [40].…”

Section: ) Audio-based Analysismentioning

confidence: 99%

“…The embeddings of contiguous audio segments are then compared and if the distance is too large then both segments are assumed to belong to different topics and therefore the video is split. Alternatively, small segments with high similarity can be clustered together in a bottom-up fashion [41]. A few methods rely on local search (e.g.…”

Section: ) Audio-based Analysismentioning

confidence: 99%

“…using genetic algorithms) to identify the temporal splits that optimize one or multiple objective functions [38], [40]. Other usage of audio transcripts of lecture videos include summaries based on word clouds with support for advanced key-word based navigation [42], abstractive textual lecture video summaries [43], generation of mappings between lecture video segments and text book sub-sections [41] and semantic annotation of lecture videos for recommender systems [44]. None of the lecture video summarization methods evaluated in this work use audio signals or transcripts.…”

Section: ) Audio-based Analysismentioning

confidence: 99%

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FCN-LectureNet: Extractive Summarization of Whiteboard and Chalkboard Lecture Videos

Davila

Setlur

et al. 2021

IEEE Access

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Recording and sharing of educational or lecture videos has increased in recent years. Within these recordings, we find a large number of math-oriented lectures and tutorials which attract students of all levels. Many of the topics covered by these recordings are better explained using handwritten content on whiteboards or chalkboards. Hence, we find large numbers of lecture videos that feature the instructor writing on a surface. In this work, we propose a novel method for extraction and summarization of the handwritten content found in such videos. Our method is based on a fully convolutional network, FCN-LectureNet, which can extract the handwritten content from the video as binary images. These are further analyzed to identify the unique and stable units of content to produce a spatial-temporal index of handwritten content. A signal which approximates content deletion events is then built using information from the spatial-temporal index. The peaks of this signal are used to create temporal segments of the lecture based on the notion that sub-topics change when large portions of content are deleted. Finally, we use these segments to create an extractive summary of the handwritten content based on key-frames. This will facilitate content-based search and retrieval of these lecture videos. In this work, we also extend the AccessMath dataset to create a novel dataset for benchmarking of lecture video summarization called LectureMath. Our experiments on both datasets show that our novel method can outperform existing methods especially on the larger and more challenging dataset. Our code and data will be made publicly available.INDEX TERMS fully convolutional networks, handwritten text detection, image binarization, lecture videos, video summarization.

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