Automatic Classification of Video Lesson Styles

• Bernadete Aquino Juiz de Fora Federal University http://orcid.org/0000-0002-6206-7003
• Eduardo Barrére Federal University of Juiz de Fora https://orcid.org/0000-0002-1598-5362
• Jairo Francisco de Souza Federal University of Juiz de Fora https://orcid.org/0000-0002-0911-7980

DOI: https://doi.org/10.5753/sbie.2022.224801

Abstract

Much has been done to investigate the effects of video lesson styles on student engagement and learning outcome. However, few studies have sought to automatically classify these styles. Thus, the current study used visual characteristics of the video lessons’ styles (presence of people and texts) and different classifiers to evaluate the proposed classification method. This automatic classification can be used by recommendation systems to suggest styles that are more adhering to student preferences and the intended learning outcome. The experiments carried out showed that the classification method used is adequate to the problem, reaching values up to 92% of accuracy.

Keywords: Educational video, video lesson style, automatic classification

References

Abreu, R., Pitangui, C., Andrade, A., Assis, L., and Silva, C. (2020). Detecção automática de estilos de aprendizagem por meio de técnicas de clusterização e classificação. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 1022–1031, Porto Alegre, RS, Brasil. SBC.

Aryal, S., Porawagama, A. S., Hasith, M. G. S., Thoradeniya, S. C., Kodagoda, N., and Suriyawansa, K. (2018). Using pre-trained models as feature extractor to classify video styles used in mooc videos. 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), pages 1–5.

Barrére, E., Souza, J., and Soares, E. (2020). Framework para segmentação temporal de vídeos educacionais. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 972–981, Porto Alegre, RS, Brasil. SBC.

Chen, H.-T. M. and Thomas, M. (2020). Effects of lecture video styles on engagement and learning. Educational Technology Research and Development, 68(5):2147–2164.

Choe, R., Scuric, Z., Eshkol, E., Cruser, S., Arndt, A., Cox, R., Toma, S., Shapiro, C., Levis-Fitzgerald, M., Barnes, G., and Crosbie, R. (2019). Student satisfaction and learning outcomes in asynchronous online lecture videos. CBE Life Sciences Education, 18.

Chorianopoulos, K. (2018). A taxonomy of asynchronous instructional video styles. International Review of Research in Open and Distributed Learning, 19(1).

Ciurez, M. A., Mihaescu, M. C., Gimenez, M., Heras, S., Palanca, J., and Julian, V. (2019). Automatic categorization of educational videos according to learning styles. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–6.

Crook, C. and Schofield, L. (2017). The video lecture. The Internet and Higher Education, 34:56–64.

Davila, K., Xu, F., Setlur, S., and Govindaraju, V. (2021). Fcnlecturenet: Extractive summarization of whiteboard and chalkboard lecture videos. IEEE Access, 9:104469–104484.

Davila, K. and Zanibbi, R. (2018). Visual search engine for handwritten and typeset math in lecture videos and latex notes. 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pages 50–55.

de Oliveira, E. S., Sales, G. L., de Sousa Pereira, P., and do Nascimento Moreira, R. (2018). Identificação automática de estilos de aprendizagem: Uma revisão sistemática da literatura. In Anais do XXVI Workshop sobre Educação em Computação, Porto Alegre, RS, Brasil. SBC.

Guo, P., Kim, J., and Rubin, R. (2014). How video production affects student engagement: An empirical study of mooc videos. In Proceedings of the First ACM Conference on Learning @ Scale Conference, pages 41–50.

Hansch, A., Hillers, L., McConachie, K., Newman, C., Schildhauer, T., and Schmidt, P. (2015). Video and online learning: Critical reflections and findings from the field. SSRN eLibrary.

Inman, J. and Myers, S. (2018). Now streaming: Strategies that improve video lectures. idea paper #68. IDEA Center, Inc.

Jayoma, J. M., Moyon, E. S., and Morales, E. M. O. (2020). Ocr based document archiving and indexing using pytesseract: A record management system for dswd caraga, philippines. In 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pages 1–6.

Kota, B. U., Stone, A., Davila, K., Setlur, S., and Govindaraju, V. (2021). Automated whiteboard lecture video summarization by content region detection and representation. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 10704–10711.

Kose, E., Taslibeyaz, E., and Karaman, S. (2021). Classification of instructional videos. Technology, Knowledge and Learning, 26.

Lackmann, S., Legér, P.-M., Charland, P., Aubé, C., and Talbot, J. (2021). The influence of video format on engagement and performance in online learning. Brain Sciences, 11:128.

Lee, G. C., Yeh, F.-H., Chen, Y.-J., and Chang, T.-K. (2017). Robust handwriting extraction and lecture video summarization. Multimedia Tools and Applications.

Lin, J., Liu, C., Li, Y., Cui, L., Wang, R., Lu, X., Zhang, Y., and Lian, J. (2019). Automatic knowledge discovery in lecturing videos via deep representation. IEEE Access, 7:33957–33963.

Ng, Y. Y. and Przybyłek, A. (2021). Instructor presence in video lectures: Preliminary findings from an online experiment. IEEE Access, 9:36485– 36499.

Ozan, O. and Ozarslan, Y. (2016). Video lecture watching behaviors of learners in online courses. Educational Media International, pages 1–15.

Rahim, M. I. and Shamsudin, S. (2019). Video lecture styles in moocs by malaysian polytechnics. In Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology, ICEMT 2019, page 64–68. Association for Computing Machinery

Rawat, Y., Bhatt, C., and Kankanhalli, M. (2014). Mode of teaching based segmentation and annotation of video lectures. 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), pages 1–4.

Rosenthal, S. and Walker, Z. (2020). Experiencing live composite video lectures: Comparisons with traditional lectures and common video lecture methods. International Journal for the Scholarship of Teaching and Learning, 14.

Sablié, M., Mirosavljevié, A., and Skugor, A. (2021). Video-based learning (vbl)—past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning, 26(4):1061–1077.

Santos Espino, J. M., Afonso Suárez, M., and Guerra Artal, C. (2016). Speakers and boards: A survey of instructional video styles in moocs. Technical Communication, 63:101–115.

Shanmukhaa, G. S., Nandita, S. K., and Kiran, M. V. K. (2020). Construction of knowledge graphs for video lectures. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pages 127– 131.

Sonia, S., Kumar, P., and Saha, A. (2021). Automatic question-answer generation from video lecture using neural machine translation. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), pages 661–665.

Urala Kota, B., Davila, K., Stone, A., Setlur, S., and Govindaraju, V. (2018). Automated detection of handwritten whiteboard content in lecture videos for summarization. In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pages 19–24.

Yousaf, M. H., Azhar, K., and Sial, H. A. (2015). A novel vision based approach for instructor’s performance and behavior analysis. In 2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA’15), pages 1–6.

Yılmaz, A., Nur Uzun, G., Zahid Gürbüz, M., and Kıvrak, O. (2021). Detection and breed classification of cattle using yolo v4 algorithm. In 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pages 1–4.