Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1631-1694 |
| Seitenumfang | 64 |
| Fachzeitschrift | International Journal of Artificial Intelligence in Education |
| Jahrgang | 35 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 21 Mai 2025 |
| Publikationsstatus | Veröffentlicht - Dez. 2025 |
Abstract
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.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Ausbildung bzw. Denomination
- Informatik (insg.)
- Theoretische Informatik und Mathematik
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in: International Journal of Artificial Intelligence in Education, Jahrgang 35, Nr. 4, 12.2025, S. 1631-1694.
Publikation: Beitrag in Fachzeitschrift › Übersichtsarbeit › Forschung › Peer-Review
}
TY - JOUR
T1 - A Closer Look into Recent Video-based Learning Research
T2 - A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
AU - Navarrete, Evelyn
AU - Nehring, Andreas
AU - Schanze, Sascha
AU - Ewerth, Ralph
AU - Hoppe, Anett
N1 - Publisher Copyright: © The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
KW - Video features
KW - Video-based learning
KW - Web-based learning
UR - http://www.scopus.com/inward/record.url?scp=105005578217&partnerID=8YFLogxK
U2 - 10.1007/s40593-025-00481-x
DO - 10.1007/s40593-025-00481-x
M3 - Review article
AN - SCOPUS:105005578217
VL - 35
SP - 1631
EP - 1694
JO - International Journal of Artificial Intelligence in Education
JF - International Journal of Artificial Intelligence in Education
SN - 1560-4292
IS - 4
ER -