Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 21 Sayı: Special Issue-IODL, 121 - 134, 17.07.2020
https://doi.org/10.17718/tojde.770948

Öz

Kaynakça

  • Abdelsalam, U. M. (2014). A Proposal Model of developing Intelligent Tutoring Systems based on Mastery Learning Keyword :, 106–118.
  • 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). 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
  • 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
  • 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.
  • 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.
  • 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/
  • 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
  • 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
  • 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
  • Kilinc, H., Firat, M., & Yüzer, T. V. (2017). Trends of video use in distance education: A research synthesis. Pegem Egitim ve Ogretim Dergisi= Pegem Journal of Education and Instruction, 7(1), 55.
  • Lecture Archive, JAIST LMS. (2020). Retrieved from https://dlc-lms.jaist.ac.jp/moodle/course/view. php?id=1472
  • Mota, P., Carvalho, N., Carvalho-Dias, E., Joao Costa, M., Correia-Pinto, J., & Lima, E. (2018). Video- Based Surgical Learning: Improving Trainee Education and Preparation for Surgery. Journal of Surgical Education, 75(3). https://doi.org/10.1016/j.jsurg.2017.09.027
  • Nazari, T., van de Graaf, F. W., Dankbaar, M. E. W., Lange, J. F., van Merrienboer, J. J. G., & Wiggers, T. (2020). One Step at a Time: Step by Step Versus Continuous Video-Based Learning to Prepare Medical Students for Performing Surgical Procedures. Journal of Surgical Education, 1–9. https:// doi.org/10.1016/j.jsurg.2020.02.020
  • Nerds, N. (2020). COVID-19 | Coronavirus: Epidemiology, Pathophysiology, Diagnostics. Retrieved May 12, 2020, from https://www.youtube.com/watch?v=PWzbArPgo-o
  • Pape-Koehler, C., Immenroth, M., Sauerland, S., Lefering, R., Lindlohr, C., Toaspern, J., & Heiss, M. (2013). Multimedia-based training on Internet platforms improves surgical performance: A randomized controlled trial. Surgical Endoscopy, 27(5), 1737–1747. https://doi.org/10.1007/ s00464-012-2672-y
  • Park, H. R., & Park, E. H. (2016). Video-aided and traditional learning method: A comparison regarding students’ knowledge retention. Indian Journal of Science and Technology, 9(40), 1–6. https://doi. org/10.17485/ijst/2016/v9i40/103261
  • Perez-Torregrosa, A. B., Diaz-Martin, C., & Ibanez-Cubillas, P. (2017). The use of video annotation tools in teacher training. Procedia-Social and Behavioral Sciences, 237, 458–464.
  • Rich, P. J., Hannafin, M., & Rich, P. J. (2010). Video Annotation Tools: Technologies to Scaffold, Structure, and Transform Teacher Reflection. https://doi.org/10.1177/0022487108328486
  • Sagorika, S., & Hasegawa, S. (2019). Video Aided Retention Tool for Enhancing Decision-Making Skills Among Health Care Professionals. In INTERNATIONAL OPEN AND DISTANCE LEARNING CONFERENCE PROCEEDINGS BOOK (pp. 305–312).
  • Shyamala, R., Sunitha, R., & Aghila, G. (2011). Towards learner model sharing among heterogeneous e-learning environments. International Journal on Computer Science and Engineering, 3(5), 2034– 2040.
  • Wang, X., Lin, L., Han, M., & Spector, J. M. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107, 106279.
  • Weeks, B. K., & Horan, S. A. (2013). A video-based learning activity is effective for preparing physiotherapy students for practical examinations. Physiotherapy, 99(4), 292–297.
  • Yang, F., Xie, H., & Li, H. (2019). Video associated cross-modal recommendation algorithm based on deep learning. Applied Soft Computing, 82, 105597.
  • Yuzer, T. V., Firat, M., & Dincer, G. D. (2016). Use of Social Networks in Open and Distance Education: What Sociology Students Share? New Trends and Issues Proceedings on Humanities and Social Sciences, 2(3), 3.

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

Yıl 2020, Cilt: 21 Sayı: Special Issue-IODL, 121 - 134, 17.07.2020
https://doi.org/10.17718/tojde.770948

Öz

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.

Kaynakça

  • Abdelsalam, U. M. (2014). A Proposal Model of developing Intelligent Tutoring Systems based on Mastery Learning Keyword :, 106–118.
  • 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). 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
  • 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
  • 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.
  • 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.
  • 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/
  • 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
  • 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
  • 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
  • Kilinc, H., Firat, M., & Yüzer, T. V. (2017). Trends of video use in distance education: A research synthesis. Pegem Egitim ve Ogretim Dergisi= Pegem Journal of Education and Instruction, 7(1), 55.
  • Lecture Archive, JAIST LMS. (2020). Retrieved from https://dlc-lms.jaist.ac.jp/moodle/course/view. php?id=1472
  • Mota, P., Carvalho, N., Carvalho-Dias, E., Joao Costa, M., Correia-Pinto, J., & Lima, E. (2018). Video- Based Surgical Learning: Improving Trainee Education and Preparation for Surgery. Journal of Surgical Education, 75(3). https://doi.org/10.1016/j.jsurg.2017.09.027
  • Nazari, T., van de Graaf, F. W., Dankbaar, M. E. W., Lange, J. F., van Merrienboer, J. J. G., & Wiggers, T. (2020). One Step at a Time: Step by Step Versus Continuous Video-Based Learning to Prepare Medical Students for Performing Surgical Procedures. Journal of Surgical Education, 1–9. https:// doi.org/10.1016/j.jsurg.2020.02.020
  • Nerds, N. (2020). COVID-19 | Coronavirus: Epidemiology, Pathophysiology, Diagnostics. Retrieved May 12, 2020, from https://www.youtube.com/watch?v=PWzbArPgo-o
  • Pape-Koehler, C., Immenroth, M., Sauerland, S., Lefering, R., Lindlohr, C., Toaspern, J., & Heiss, M. (2013). Multimedia-based training on Internet platforms improves surgical performance: A randomized controlled trial. Surgical Endoscopy, 27(5), 1737–1747. https://doi.org/10.1007/ s00464-012-2672-y
  • Park, H. R., & Park, E. H. (2016). Video-aided and traditional learning method: A comparison regarding students’ knowledge retention. Indian Journal of Science and Technology, 9(40), 1–6. https://doi. org/10.17485/ijst/2016/v9i40/103261
  • Perez-Torregrosa, A. B., Diaz-Martin, C., & Ibanez-Cubillas, P. (2017). The use of video annotation tools in teacher training. Procedia-Social and Behavioral Sciences, 237, 458–464.
  • Rich, P. J., Hannafin, M., & Rich, P. J. (2010). Video Annotation Tools: Technologies to Scaffold, Structure, and Transform Teacher Reflection. https://doi.org/10.1177/0022487108328486
  • Sagorika, S., & Hasegawa, S. (2019). Video Aided Retention Tool for Enhancing Decision-Making Skills Among Health Care Professionals. In INTERNATIONAL OPEN AND DISTANCE LEARNING CONFERENCE PROCEEDINGS BOOK (pp. 305–312).
  • Shyamala, R., Sunitha, R., & Aghila, G. (2011). Towards learner model sharing among heterogeneous e-learning environments. International Journal on Computer Science and Engineering, 3(5), 2034– 2040.
  • Wang, X., Lin, L., Han, M., & Spector, J. M. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107, 106279.
  • Weeks, B. K., & Horan, S. A. (2013). A video-based learning activity is effective for preparing physiotherapy students for practical examinations. Physiotherapy, 99(4), 292–297.
  • Yang, F., Xie, H., & Li, H. (2019). Video associated cross-modal recommendation algorithm based on deep learning. Applied Soft Computing, 82, 105597.
  • Yuzer, T. V., Firat, M., & Dincer, G. D. (2016). Use of Social Networks in Open and Distance Education: What Sociology Students Share? New Trends and Issues Proceedings on Humanities and Social Sciences, 2(3), 3.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Safinoor Sagorıka Bu kişi benim 0000-0002-4328-1135

Shinobu Hasegawa Bu kişi benim 0000-0002-0892-9629

Yayımlanma Tarihi 17 Temmuz 2020
Gönderilme Tarihi 26 Temmuz 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 21 Sayı: Special Issue-IODL

Kaynak Göster

APA Sagorıka, 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, 21(Special Issue-IODL), 121-134. https://doi.org/10.17718/tojde.770948