Redesigning the Online Video Lecture Player to Promote Active Learning

Walk, Ian; Yim, Arnold; Novak, Ed; Reiss, Charles; Graham, Daniel

doi:10.1109/fie44824.2020.9273898

Cited by 1 publication

(9 citation statements)

References 9 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%

“…For image-related data, many studies suggested approaches for slide transition detection and video segmentation [17,29,32,64,67,69,71,72,77,83,110,115,153,160,178,205,220,262,265,273]. For audio tracks, various studies carried out speech recognition to generate transcripts [14,16,21,24,29,31,40,54,64,69,71,102,106,109,142,161,182,202,250,267,273]. In the next subsections, we discuss the tasks and technologies in detail according to each type of tool.…”

Section: Audio Featuresmentioning

confidence: 99%

“…Kohen-Vacs et al [120] introduced a tool to embed quizzes and announcements along the playback into the video. Walk et al [250] included quizzes in the video with the aim of enhancing active learning. Wachtler et al [249] presented an application that can include several types of questions such as single-and multiple-choice questions, as well as questions to the instructor.…”

Section: Interactive Toolsmentioning

confidence: 99%

“…Jordán et al [108] built a system that considers not only the characteristics of the current video but also previous videos watched by the learner. Walk et al [250] made use of word embeddings constructed from the video transcript and compared them against the user search query to retrieve the most similar video segment.…”

Section: Recommender and Retrieval Systemsmentioning

confidence: 99%

“…Based on learner's activity: General analytic tools exploited log data of learner activity from e-learning platforms to show how videos are used. Walk et al [250] developed a tool that shows the audience retention for specific video segments, the number of times the segment was re-watched, and how much time learners spent on it. Wachtler et al [249] presented a tool that provided an analysis about the video (e.g., number of views) and also about the student (e.g., extent to which the video was watched).…”

Section: Analytic 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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