A Category-based Framework of a Self-improving Instructional Planner
スポンサーリンク
概要
- 論文の詳細を見る
To have an instructional plan guide the learning process is significant to various teaching styles and an important task in an ITS. Though various approaches have been used to tackle this task, the compelling need is for an ITS to improve on its own the plans established in a dynamic way. We hypothesize that the use of knowledge derived from student categories can significantly support the improvement of plans on the part of the ITS. This means that category knowledge can become effectors of effective plans. We have conceived a Category-based Self-improving Planning Module (CSPM) for an ITS tutor agent that utilizes the knowledge learned from learner categories to support self-improvement. The learning framework of CSPM employs unsupervised machine learning and knowledge acquisition heuristics for learning from experience. We have experimented on the feasibility of CSPM using recorded teaching scenarios.
- Information and Media Technologies 編集運営会議の論文
著者
-
NUMAO MASAYUKI
The Institute of Scientific and Industrial Research, Osaka University
-
Numao Masayuki
The Institute Of Scientific And Industrial Research Osaka University
-
Legaspi Roberto
The Institute Of Scientific And Industrial Research Osaka University
-
Sison Raymund
College Of Computer Studies De La Salle University - Manila
-
Sison Raymund
College of Computer Studies, De La Salle University
関連論文
- SBSOM : Self-Organizing Map for Visualizing Structure in the Time Series of Hot Topics(Text Mining I)
- SBSOM : Self-Organizing Map for Visualizing Structure in the Time Series of Hot Topics(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- Visualization of Damage Progress in Solid Oxide Fuel Cells
- Inductive Logic Programming for Multiple-Part Data : Applications on Structure-Activity Relationship Studies
- Partial Rule Weighting Using Single-Layer Perceptron(Artificial Intelligence III)
- Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure(Scientific Data Mining)
- Partial Rule Weighting Using Single-Layer Perceptron(Artificial Intelligence III)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- Multiple-Instance Learning Based Heuristics for Mining Chemical Compound Structure(Scientific Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- A Category-based Framework of a Self-improving Instructional Planner
- Feature Discovery in Temporal Data(Artificial Intelligence III)
- Preprocessing Planning for Data Mining(Artificial Intelligence I)
- Preprocessing Planning for Data Mining(Artificial Intelligence I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- Feature Discovery in Temporal Data(Artificial Intelligence III)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- A Category-based Framework of a Self-improving Instructional Planner