| 研究課題/領域番号 |
21H03482
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| 研究機関 | 大阪大学 |
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研究代表者 |
ORLOSKY JASON 大阪大学, サイバーメディアセンター, 招へい准教授 (10815111)
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| 研究分担者 |
白井 詩沙香 大阪大学, サイバーメディアセンター, 講師 (30757430)
清川 清 奈良先端科学技術大学院大学, 先端科学技術研究科, 教授 (60358869)
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| 研究期間 (年度) |
2021-04-01 – 2025-03-31
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| キーワード | simulation / virtual reality / eye tracking / agents / remote interaction / education |
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| 研究実績の概要 |
In this fiscal year, we have begun to develop several fundamental parts of the project. This includes the design of avatars as well as tests to determine whether the avatars elicit certain responses in users. The avatars are rigged with various emotional states, which can be applied in the remote learning environment. Using the lip trackers, we can also replicate and track remote participant facial expressions.
We have also designed a remote interaction environment in which mutual gaze can be visualized between an instructor and student in a remote capacity. The instructor obtains a miniature version of both the remote environment and the user within that environment. The system already facilitates mutual grasping of and gaze onto different objects within the scene, which will eventually be the learning or training materials with which the remote participants interact.
Lastly, we have continued our work on developing better models for classifying the understanding of texts in the context of education and personal study. This included the exploration of support vector machines and random forest algorithms to classify difficulties during long periods of learning and studying. The classification is focused on text-based learning, which we hypothesize can eventually be applied to 3D learning.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
In general, the project is proceeding on track. We have two publications that are currently being written, and others are planned for later in 2022. The remote environment setup has also started slightly early, though the entire project is roughly on schedule on average.
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| 今後の研究の推進方策 |
In the next year, we plan to finish development of the remote learning environment. This will also include the integration of avatars into the simulated learning space. Later in the year, we will begin to develop and test the intelligent refinement of agent activities based on the learning environment participants' needs.
This also involves applying the aforementioned machine learning models to a 3D space. We will begin the integration of the methods that were applied to text-based learning spaces to 3D interactive learning scenarios. We will then gather feedback from learners and refine the learning tools and simulation as necessary.
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