Towards quality monitoring and managing for online learning
Project/Area Number |
22K12299
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 62030:Learning support system-related
|
Research Institution | The University of Aizu |
Principal Investigator |
Truong CongThang 会津大学, コンピュータ理工学部, 上級准教授 (40622957)
|
Project Period (FY) |
2022-04-01 – 2025-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2024: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2023: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
Keywords | Quality of experience / Adaptive streaming / Online learning / Quality model / Media analysis / Multi-feature learning |
Outline of Research at the Start |
We will first investigate and evaluate existing quality models and transmission methods of adaptive streaming. The models and methods will be improved and adapted for online learning, considering the presence of media objects. Then, solutions of monitoring and managing quality will be developed.
|
Outline of Annual Research Achievements |
In AY2022, we investigated problems of Quality of Experience (QoE) and video delivery for online learning. We evaluated of a large number of quality models for both PC and mobile users. Because online learning is mostly received via PC displays and has long durations, we found that just a few of quality models were appropriate for measuring the QoE at the learner’s side. Besides, the potential of multi-feature learning approach was studied for modeling. For video delivery, a new solution was proposed to send the same video to multiple users. Our solution employed scalable video coding and multicast mechanism to effectively deliver a video to many users in a session. We also investigated key issues and techniques in system implementation for QoE management for post-pandemic online learning.
|
Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
In the last year, we have successfully finished the investigation of both quality models and media delivery mechanism. The research has progressed rather smoothly as planned. It is found that existing quality models are still limited and need significant improvements. Thus, our current focus is on developing effective quality models to support both good perception and good semantic understanding for users.
|
Strategy for Future Research Activity |
In the AY2023, we will focus on the development of new quality models. The main approach is to take into account multiple features of media contents. Because in online learning, the quality of media contents may affect both the perception and understanding of users, the features will be extracted by considering both the perceptual aspect and semantic aspect of contents. New deep learning methods will be applied for efficient and effective deployment of quality models.
|
Report
(1 results)
Research Products
(5 results)