Semi-automated Student Feedback and Theory-Driven Video-Analytics: An Exploratory Study on Educational Value of Videos

Eradze, Maka; Dipace, Anna; Fazlagic, Bojan; Pietro, Anastasia Di

doi:10.1007/978-3-030-67435-9_3

Cited by 1 publication

(4 citation statements)

References 16 publications

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“…audio features [56,211,212,214] and visual features [56,212,214], instructor's behavior characteristics [12,56,189,212,214,237,243], design principles and guidelines [60,140,185,212], and interactive features [56,82]. The corresponding findings are presented in the next section (Section 6).…”

Section: Data Analysis Studiesmentioning

confidence: 98%

“…Pace-related: automatic pauses, pauses controlled by the learner, type of pauses, fast pace. Interactive features [11, 15, 20, 33, 34, 41, 46, 47, 51, 53, 56, 62, 82, 84, 91, 100, 106, 110, 113, 114, 118-120, 125, 129, 130, 132, 142, 149-151, 154, 157, 162, 172, 175, 178, 188, 199, 208, 210, 217, 222, 234, 238, 240, 247-250, 259, 277] Quizzes embedded in the video, virtual reality and 360-degree characteristics, in-video annotations, feedback to the learner, games, hyperlinks, interface elements for navigation Production style [6,30,42,45,49,55,56,87,88,93,94,136,140,180,181,190,191,197,207,212,214,223,224,229,230,236] Khan-style, with a talking head, dialogue-style or monologue-style, slide-based, with animations, paper-based explanations, classroom-style videos, Instructional design and principles [5,58,60,75,95,101,135,140,141,169,185,212,226,241,246,...…”

Section: Audio Featuresmentioning

confidence: 99%

“…The majority of data analysis studies focused on the understanding of patterns in learning behavior. Furthermore, around half of the data analysis papers have also tried to understand the impact of those patterns on a specific variable that measures learning effectiveness [9,12,27,44,45,60,70,82,94,126,137,146,200,213,227,236,260]. In the same way as in the controlled experiment papers, the impact of video characteristics has been studied mostly on the learning outcome [9,27,45,70,94,126,146,200,213,227,236,260] and on engagement [12,45,70,82,94,146,236].…”

Section: Data Analysis Studiesmentioning

confidence: 99%

“…Figure 8 sorts the characteristics studied in data analysis articles according to their frequency. We found that data analysis studies usually made use of learner's activity characteristics by taking advantage of system logs (e.g., play, pause) [9,12,44,45,57,60,79,99,126,137,139,140,146,167,189,200,203,212,213,227,239,243,260,266]. Other characteristics often analyzed were metadata and video transcripts [12,56,60,70,94,137,167,203,211,212,216,218,243].…”

Section: Data Analysis Studiesmentioning

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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