9. TOKYO JOHO UNIVERSITY
Ajaxによるゲームロジックと通信部分の分離
Ajaxの利点
サーバとの非同期通信
クライアント側でのゲームロジックの実行
Webページの部分更新
リアルタイムWebゲームの課題解決
Ajaxによる内部処理と通信の分離
Dead Reckoningによる補間処理
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t
t
Remote
Local
transmission
interval
MVC loop’s interval
10. TOKYO JOHO UNIVERSITY
Dead Reckoningによる予測・補間
概要
過去データを基に遠隔アバタの状態を時々刻々予測
利点
通信遅延の影響の軽減
通信帯域の節約
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位置 A(t)
時刻 T
予測・補間
t-ti
ti-2 ti-1 ti
A(ti)
A(ti-1)
A(ti-2)
t
42. TOKYO JOHO UNIVERSITY
学修データ収集システム
学修活動と学年に応じて動的にアンケートを生成
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Kawano, Yoshihiro, & Kawano, Yuka (2021). Development of Learning Systems for Children to Promote Self-Directed Choosing
of Learning Tasks. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 12(3), 60-77.
43. TOKYO JOHO UNIVERSITY
学修フィードバックシステム
44
アチーバー ソーシャライザー
エクスプローラー
バートルテストに基づき各クラスタを命名
Y. Kawano, Y. Kawano, "A Proposal of Learning Feedback System for Children to Promote Self-directed Learning",
The 24th International Conference on Network-Based Information Systems (NBiS-2021) (Taichung, Taiwan), 2021.9.
44. TOKYO JOHO UNIVERSITY
学修データ収集システムの構成図
Architecture
Front end: Vue.js (UI framework for single page application)
Questionnaire items: SQL via Web API
Answer data: Document-oriented NoSQL (MongoDB)
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We have proposed online/onsite hybrid community activity called “Walk Adventure”.
In this event, the online and onsite participants cooperate with each other to compete for clear time while visiting local spots in the manner of a walk rally. To reduce the time required to answer the questionnaire at the event using the collection system, the questionnaire items were limited to the minimum necessary. And the feedback system based on the learning-data collected in this event and simple clustering was developed.
This is screenshot of the feedback system.
In this implementation, the learning-data was classified into three clusters. Labeling was conducted appropriately based on the eigenvalues of the first and second principal components. Each principal component was wording chosen to express the difference in features instead of superiority in skills. In this case, first principal component is labeled "Concentrative" and "Dialogical". Second principal component is labeled "Enjoy game" and "Enjoy event“.
The naming of the clusters is based on the Bartle test, a well-known gamer classification about gamification.
Achiever, Socializer, and Explorer.
When the learner responds to the collection system, the clustering results are presented in red marker.
This figure shows system architecture of Learning-data Collection System.
In front end, we adopt Vue.js which is UI framework for single page application.
This system dynamically constructs questionnaire from the SQL via the Web API, based on the grades and learning activities selected by children.
Answer data is recorded to MongoDB which is Document-oriented NoSQL as JSON format.