SavedAdd your journal

Journals / Turkish Online Journal of Distance Education / 2020 / Cilt: 21 - Sayı: Special Issue-IODL

DESIGN OF VIDEO AIDED RETENTION TOOL FOR THE HEALTH CARE PROFESSIONALS IN SELF-DIRECTED VIDEO-BASED LEARNING

Safinoor SAGORIKA
Shinobu HASEGAWA
Pages
121–134
Publication Date
2020-07-17
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using such kind of data, attention, and retention will be determined and filtered reels, recommendations, interactive videos will be provided to the learners.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Health Care Professionals (HCPs) depend on self-directed learning by watching medical videos.
📊
Key Findings & Data:The VART consists of a combination of video tracking, analyzing, and filtering tools, with the integration of domain model, learners’ model, and e-teaching strategy model to aid in self-directed learning.
💡
Conclusion & Impact:Using such kind of data, attention, and retention will be determined and filtered reels, recommendations, interactive videos will be provided to the learners.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Video Aided Retention Tool (VART) · Video-based Learning · Self-directed Learning

Abstract

Health Care Professionals (HCPs) depend on self-directed learning by watching medical videos. In the traditional video learning system, it is difficult to identify the important videos from the huge data set and to find the essential inside parts of a long video. In addition, it is hard to know learners’ preferences inside the video parts, including duration and repetition of watching. If the system could know the attention and retention process of each learner, it could change the way to show the video. Accordingly, this research proposes to design the Video Aided Retention Tool (VART) system for analyzing video content to improve self-directed video-based learning among HCPs. The VART consists of a combination of video tracking, analyzing, and filtering tools, with the integration of domain model, learners’ model, and e-teaching strategy model to aid in self-directed learning. The proposed VART will pick important videos on a single topic and put automatic indexes to represent the essential parts of video content. It will also track the learner’s ID, content preference, monitor watching duration, and repetition of the content. Using such kind of data, attention, and retention will be determined and filtered reels, recommendations, interactive videos will be provided to the learners.

🔖

Cite This Article

SAGORIKA, S., HASEGAWA, S. (2020). DESIGN OF VIDEO AIDED RETENTION TOOL FOR THE HEALTH CARE PROFESSIONALS IN SELF-DIRECTED VIDEO-BASED LEARNING. Turkish Online Journal of Distance Education, Cilt: 21 - Sayı: Special Issue-IODL, 121–134. https://ajindex.com/articles/137259

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Abdelsalam, U. M. (2014). A Proposal Model of developing Intelligent Tutoring Systems based on Mastery Learning Keyword:, 106–118.
  2. Brown, P. (2014). What is the Domain Model in Domain Driven Design? Retrieved from https://culttt. com/2014/11/12/domain-model-domain-driven-design/ Brusilovsky, P., & Millan, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In The adaptive web (pp. 3–53).
  3. Springer. Cebrian-de-la-Serna, M; Bartolome-Pina, A. (2015). Study of Portfolio in the Practicum: an Analysis of PLE- Portfolio, 21. Clifton, A., & Mann, C. (2011). Can YouTube enhance student nurse learning? Nurse Education Today, 31(4), 311–313. https://doi.org/10.1016/j.nedt.2010.10.004
  4. Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers and Education, 78, 312–320. https://doi.org/10.1016/j.compedu.2014.06.018
  5. El Bachari, E., Abelwahed, E. H., & El Adnani, M. (2012). An adaptive teaching strategy model in e-learning using learners’ preference: LearnFit framework. International Journal of Web Science 3, 1(3), 257–274.
  6. Friedl, R., Hoppler, H., Ecard, K., Scholz, W., Hannekum, A., Ochsner, W., & Stracke, S. (2006). Multimedia-driven teaching significantly improves students’ performance when compared with a print medium. The Annals of Thoracic Surgery, 81(5), 1760–1766.
  7. Giannakos, M., Chorianopoulos, K., Ronchetti, M., Szegedi, P., & Teasley, S. (2014). Video-based learning and open online courses. Giannakos, M. N., Chorianopoulos, K., Ronchetti, M., Szegedi, P., & Teasley, S. D. (2013). Analytics on video-based learning. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 283–284). H5P. (n.d.). Retrieved June 7, 2020, from https://h5p.org/
  8. Hasegawa, S., & Dai, J. (2015). A Ubiquitous Lecture Archive Learning Platform with Note-Centered Approach, 9173, 294–303. https://doi.org/10.1007/978-3-319-20618-9
  9. Hasegawa, S., Kashihara, A., & Toyoda, J. (2003). A local indexing for learning resources on WWW. Systems and Computers in Japan, 34(3), 1–9. https://doi.org/10.1002/scj.10231
  10. Jang, H. W., & Kim, K. J. (2014). Use of online clinical videos for clinical skills training for medical students: Benefits and challenges. BMC Medical Education, 14(1). https://doi.org/10.1186/1472- 6920-14-56
  11. Kilinc, H., Firat, M., & Yüzer, T. V. (2017). Trends of video use in distance education: A research syn