People increasingly use videos on the Web for learning, be it for daily tasks in formal or informal educational settings. To enhance this type of learning, scientists are continuously conducting experiments, proposing guidelines, analyzing data, and researching artificial intelligence methods for tool development. In this comprehensive review, we analyze 257 articles (using PRISMA guidelines) on video-based learning from a technological perspective for the period from 2016 to 2021. One of the aims is to identify video characteristics that support learning as explored by previous research. Based on our analysis, we suggest a taxonomy that 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 (play, pause, etc.), (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative methodological approaches: (1) tool support of video-based learning, (2) controlled experiments, (3) data analysis studies, and (4) design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learners’ activities, and interactive features. Tools that aid learning through videos frequently utilize text from transcripts, video frames, and images. The learner’s activity is heavily explored through log files in data analysis studies, and interactive features are frequently scrutinized in controlled experiments. As further contributions, we contrast research findings on how video characteristics affect learning effectiveness, report on tasks and technologies used to develop tools, and summarize design guideline trends to produce learning videos. Our findings provide actionable insights for the design of intelligent educational systems that better support video-based learning. © The Author(s) 2025.
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