Abstract
Video-based learning offers a learner a self-paced, lucid, memorizable, and a flexible way of learning. The availability of abundant educational video materials on the web has certainly abetted an individual’s learning means. But the lack of necessary information about the videos makes it difficult for the learner to search and select the exact video as per his/her requirement and suitability in terms of the learner’s learning capability and the material’s relevancy, difficulty level, etc. Educational video recommendation systems also suffer from a similar problem. Extracting the required metadata, by different means, from the learning videos is a plausible solution. Despite the credible research efforts on video metadata extraction, the problem of educational video metadata extraction has been overlooked. This paper proposes a comprehensive approach to extract educational metadata from a learning video. A semiautomatic mechanism that includes manual and computational approaches is introduced for metadata extraction and to evaluate the values of these metadata. Along with identifying a set of specific metadata attributes from IEEE LOM, few additional attributes are suggested which are imperative to assess the suitability of a video-based learning object in terms of the personalized preference and suitability of a learner. The test results are validated by comparing with the manually extracted metadata by experts, on the same videos. The outcome establishes the promising effectiveness of the approach.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Algur, S. P., & Bhat, P. (2016). Web Video Mining: Metadata Predictive Analysis using Classification Techniques. International Journal of Information Technology and Computer Science, 2, 68–76.
Alves, M. B., Damásio, C. V., & Correia, N. (2015). Extracting facebook multimedia contents metadata as media annotation. In P. Klinov & D. Mouromtsev (Eds.), Knowledge Engineering and Semantic Web (pp. 243–252). Moscow: Springer.
Anusuya, M. A., & Katti, S. K. (2009). Speech Recognition by Machine A Review. International Journal of Computer Science and Information Security, 6(3), 181–205.
Balagopalan, A. et al. (2012). Automatic keyphrase extraction and segmentation of video lectures . Kerala, IEEE International Conference on Technology Enhanced Education (ICTEE).
Balasubramanian, V., Doraisamy, S. G., & Kanakarajan, N. K. (2016). A multimodal approach for extracting content descriptive metadata from lecture videos. Journal of Intelligent Information Systems, 46(1), 121–145.
Bolettieri, P., Falchi, F., Gennaro, C., & Rabitti, F. (2007). Automatic metadata extraction and indexing for reusing e-learning multimedia object. Bavaria: ACM Workshop on The Many Faces of Multimedia Semantics.
Changuel, S., & Labroche, N. (2012). Content independent metadata production as a machine learning problem. In P. Perner (Ed.), Machine learning and data mining in pattern Recognition (pp. 306–320). Heidelberg: Springer.
CSU Northridge Oviatt Library (2019). What are digital learning objects?. [Online] Available at: https://library.csun.edu/docs/ScholarWorks/LearningObjectsClarification.pdf. Accessed 12 Mar 2019.
Gibbon, D. C., Liu, Z., Basso, A., & Shahraray, B. (2013). Automated content metadata extraction services based on MPEG standards. The Computer Journal, 56(5), 628–645.
Gruber, T. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 45(5–6), 907–928.
Gunter, G. A., & Kenny, R. (2004). Video in the classroom: learning objects or objects of learning? Chicago: Association for Educational Communications and Technology.
Hentschel, C., Blümel, I., & Sack, H. (2013). Automatic annotation of scientific video material based on visual concept detection. Graz: International Conference on Knowledge Management and Knowledge Technologies.
IEEE Computer Society. (2002). 1484.12.1 IEEE Standard for Learning Object Metadata. New York: The Institute of Electrical and Electronics Engineers.
Institute for Teaching and Learning Innovation (2018). Pedagogical benefits. [Online] Available at: http://www.uq.edu.au/teach/video-teach-learn/ped-benefits.html. Accessed Sept 2018.
Khurana, K., & Chandak, M. B. (2013). Study of various video annotation techniques. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 909–914.
Kothawade, A. Y., & Patil, D. R. (2016). Retrieving Instructional Video Content from Speech and Text Information. In S. Satapathy, Y. Bhatt, A. Joshi, & D. Mishra (Eds.), Advances in Intelligent Systems and Computing (pp. 311–322). Singapore: Springer.
Lee, H.-Y., et al. (2014). Spoken knowledge organization by semantic structuring and a prototype course lecture system for personalized learning. IEEE/ACM Transaction on Audio, Speech, and Language Processing, 22(5), 883–898.
Linfield College (2018). Why use digital video? [Online] Available at: https://www.linfield.edu/tls/blendedlearning/why-use.html. Accessed Sept 2018].
LoveToKnow (2018). Keyword outline example. [Online] Available at: http://examples.yourdictionary.com/keyword-outline-examples.html. Accessed Sept 2018.
Maniar, N., Bennett, E., Hand, S., & Allan, G. (2008). The effect of mobile phone screen size on video based learning. Journal of Software, 3(4), 51–61.
Mori, S., Nishida, H., & Yamada, H. (1999). Optical character recognition. New York: John Wiley & Sons.
Noy, N. F., & Mcguinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford: Stanford University.
Othman, E. H., Abdelali, S., & Jaber, E. B. (2016). Education data mining: Mining MOOCs video using meta data based approach. Tangier: IEEE International Colloquium on Information Science and Technology (CiSt).
Pal, S., Mukhopadhyay, M., Pramanik, P. K. D., & Choudhury, P. (2018). Assessing the learning difficulty of text-based learning materials. Da Nang city: Frontiers of Intelligent Computing: Theory and Application.
Pal, S., Pramanik, P. K. D. & Choudhury, P., 2019. A step towards smart learning: Designing an interactive video-based M-learning system for educational institutes. International Journal of Web-Based Learning and Teaching Technologies , 14(4).
Pramanik, P. K. D., Choudhury, P. & Saha, A., 2017. Economical Supercomputing thru smartphone crowd computing: An assessment of opportunities, benefits, deterrents, and applications from India’s Perspective. Coimbatore, International Conference on Advanced Computing and Communication Systems.
Radha, N. (2016). Video retrieval using speech and text in video. Coimbatore: International Conference on Inventive Computation Technologies (ICICT).
Rafferty, J., Nugent, C., Liu, J. & Chen, L. (2015). Automatic metadata generation through analysis of narration within instructional video. Journal of Medical System, 39, (9).
Rangaswamy, S., Ghosh, S., Jha, S., & Ramalingam, S. (2016). Metadata extraction and classification of YouTube videos using sentiment analysis. Orlando: IEEE International Carnahan Conference on Security Technology (ICCST).
Rouse, M. (2005). Ontology. [Online] Available at: https://whatis.techtarget.com/definition/ontology. Accessed Sept 2018.
Singh, R. K., & Singh, R. (2014). Emerging role of ontology in semantic web:developmental prospective. International Journal of Advanced Research in Computer Science and Software Engineering, 4(7), 301–307.
Spyrou, E., Tolias, G., Mylonas, P., & Avrithis, Y. (2009). Concept detection and keyframe extraction using a visual thesaurus. Multimedia Tools and Applications, 41(3), 337–373.
Truong, T.-D., et al. (2018). Video search based on semantic extraction and locally regional object proposal. In K. Schoeffmann et al. (Eds.), MultiMedia Modeling (pp. 451–456). Bangkok: Springer.
VARK Learn Limited (2018). The VARK Modalities. [Online] Available at: http://vark-learn.com/introduction-to-vark/the-vark-modalities/. Accessed 9 12 2018].
Waitelonis, J., Plank, M., & Sack, H. (2016). TIB|AV-Portal: Integrating Automatically Generated Video Annotations into the Web of Data. In N. Fuhr, L. Kovács, T. Risse, & W. Nejdl (Eds.), Research and advanced technology for digital libraries (pp. 429–433). Hannover: Springer.
Yang, H., & Meinel, C. (2014). Content based lecture video retrieval using speech and video text information. IEEE Transactions on Learning Technologies, 7(2), 142–154.
Yang, H., et al. (2011). Lecture video indexing and analysis using video OCR technology. Dijon: International Conference on Signal Image Technology & Internet-Based Systems.
Zhou, H., & Pang, G. K. (2010). Metadata extraction and organization for intelligent video surveillance. Xi'an: IEEE International Conference on Mechatronics and Automation.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pal, S., Pramanik, P.K.D., Majumdar, T. et al. A semi-automatic metadata extraction model and method for video-based e-learning contents. Educ Inf Technol 24, 3243–3268 (2019). https://doi.org/10.1007/s10639-019-09926-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-019-09926-y