Safinoor Sagorika awarded PhD Degree
Md. Monirul Islam,
January 13, 2022
Ms. Safinoor Sagorika, a former student of the Department of Information Science and Library Management, University of Dhaka, and life member of LAB and BALID, has been awarded a Doctor of Philosophy (PhD) degree under the Graduate School of Advanced Science and Technology, by Japan Advanced Institute of Science and Technology (JAIST), Japan on 24 December 2021. Her primary research topic was “Design and Development of Video Aided Retention Support System for Enhancing Disaster Survival Skills (DSS) Among International Students” which is a tiny part of the system engineering mainly focused on “learning technology“.
As per the degree requirement, Dr. Sagorika also conducted her minor research project on “Adopting Orphan Migrants in Japanese Elderly Care Services: A Diversified Model for Ishikawa Prefecture”. She attended four international conferences in Thailand, Turkey, and Japan during her PhD journey and published four international conference proceedings. She also authored three journal articles from major research. In addition, she has successfully completed 22 credits from 11 courses.
Dr. Sagorika conducted her PhD studies under the MEXT (Monobugakusho) scholarship. In addition, she was awarded Educational Internship Project in Malaysia, 2018, by JAIST, Lab funding for attending KICSS 2018 Conference in Pattaya, Thailand, and JAIST Research Grant, 2019 for attending IODL-2019 Conference, Eskişehir, Turkey, JSPS KAKENHI Grant in 2021. She served as a Teaching Assistant (TA) for several courses and Technical Assistant (TA) for supporting online lectures in JAIST.
Dr. Sagorika’s research mainly focused on analyzing, designing, developing, implementing, and evaluating the Video Aided Retention Tool (VART) to provide learning support through self-directed video and animation-based learning. The VART system is the combination of video fractioning, indexing, tracking, analyzing, filtering, and recommendation tools, with the integration of domain model, students’ model, and e-teaching strategy model to assist in self-directed video-based learning. The system organizes and makes virtual fragmentation of each long video into different significant chunks in small, highly focused materials, add meaningful local indexes for each fraction and identifies interrelated and prerequisite Learning Objects (LOS) in the video. Similarly, it organizes short videos and structures them based on complementary and prerequisite relations among the LOs. Furthermore, it tracks each student’s ID, content preferences, duration, repetition, most watching parts, etc., to realize their attention and retention process. Based on the detailed watching history data, VART analyzes and determines each student’s learning needs and recommends, filters, and delivers essential video parts sequentially to support the video-based learning process.
In the implementation phase, the researcher experimented with the international students of JAIST to identify the appropriate content structuring system for acquiring DSS, learning process and progress of the learners, and understand the effectiveness of the proposed method. Based on the detailed watching history data, the group retention maps and individual learners’ learning path visualization maps were produced, which assists both the learners and the educators to visualize the learners’ interactions with the contents with a quick look in the online learning environment. This technique is developed to monitor each student’s learning progress and assist them in adjusting to the content structure dynamically from the system point of view. If a learner misses any important content from the list, the maps can indicate that missed part/parts. Similarly, after viewing the learning path map, learners can avoid the contents that he/she has already mastered. Finally, the research provided a couple of mathematical algorithms to create necessary recommendations, i.e., cold standby and hot standby recommendations, including collaborative filtering for the groups and each learner considering different conditions in their learning process.
In the evaluation phase, the researcher further conducted post-experiment feedback among the participants to compare the changing impact and realize the improvement of the learning behaviour and learning outcome among the learners before and after implementing the system. Besides, the summative assessment was done to summarize and modify the functions where necessary. Finally, a new system was proposed to implement the disaster survival education/training domain.
It is also worth mentioning that the research first started working with the medical videos to create a support system in the selected medical domain for the Health Care Professionals (HCPs). The study also designed and proposed VART functions for the medical field, published as a journal article and a conference paper. Due to the COVID-19 situation, it was impossible to conduct experiments with Japanese doctors and nurses in the hospital in Japan. Nevertheless, the research found that medical videos are enormous and complex compared to DSS videos. In the case of medical videos, filtering expected contents from the ocean of resources and making recommendations are considered the main priority with other requirements.
On the other hand, selecting appropriate content and identifying proper content structure, representing contents with small chunks and meaningful indexes so that international students get motivated to learn and acquire essential skills from the DSS video contents are considered a vital criteria. The research found that the proposed VART system is implementable and adaptable for both the medical and DSS domains because of its adaptive features and flexibility.
The target learners, contents, and learning requirements might be different, but the video-based domain’s basic features are almost similar. So, the VART system could be customized based on various learning needs; primarily the domain of learning has a wide range to be adopted by numerous video-based and online learning platforms.