Analysing Emotions on Lecture Videos using CNN AND HOG (Workshop Paper)

Godavarthi, Deekshith; Aswath, S.; Mishra, Debanik; Jayashree, R.

doi:10.1109/bigmm50055.2020.00072

Cited by 2 publications

(3 citation statements)

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“…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). Regarding instructor behavior, speech-related characteristics [12,14,28,32,56,98,105,107,111,111,160,189,212,214,221,243] have been used more often than body-related characteristics [18,19,35,56,80,96,98,145,214,237,254,263] (see Figure 9). Less investigated characteristics were instructional design characteristics, which were mainly treated in articles on guidelines for the design and creation of videos [58,75,…”

Section: Discussionmentioning

confidence: 99%

“…For classifiers and predictors the main task was to predict the learning outcome using learner's activity data [27,68,122,133,143,165,173]. Other, but less frequent tasks were discipline classification [17,31,54,184,228], instructor behavior prediction [35,80,207,263], and dropout prediction [66,104].…”

Section: Discussionmentioning

confidence: 99%

“…Instructor behavior prediction: Chen et al [35] built a neural network to predict teachers' enthusiasm using facial expressions, voice, volume, and pose characteristics. Godavarthi [80] also used facial landmarks characteristics but to predict instructor emotions using a CNN. Sharma et al [207] predicted video liveliness (energetic or active) using two different long-short-term memory networks fed with audio features, visual features from the instructor actions, and production setup (e.g., instructor sitting or standing, slide-based video or whiteboard-based video).…”

Section: Classifiers and Predictorsmentioning

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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