A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
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Abstract (arXiv)
People increasingly use videos on the Web as a source for learning. 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 for the period from 2016 to 2021. 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 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 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 of design guidelines to produce learning videos
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
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- 2026Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Modelscited by 0
- 2026SGA: Plug&Play Geometric Verification for Educational Video Synthesiscited by 0
- 2026AutoCue: Multimodal LLM-Assisted Externalization of Implicit Inputs as Instructional Visual Cues in Screencast Tutorialscited by 0
- 2026AutoCue: Multimodal LLM-Assisted Externalization of Implicit Inputs as Instructional Visual Cues in Screencast Tutorials5 Aug 2026
- 2026SGA: Plug&Play Geometric Verification for Educational Video Synthesis20 Jul 2026
- 2026Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Models6 Apr 2026
- 2025Unified Interactive Multimodal Moment Retrieval via Cascaded Embedding-Reranking and Temporal-Aware Score Fusion15 Dec 2025
- 2025SciCom Wiki: Fact-Checking and FAIR Knowledge Distribution for Scientific Videos and Podcasts12 May 2025
- 2024Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation8 Aug 2024