政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/69780

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113311/144292 (79%)
Visitors : 50926034 Online Users : 866

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/69780

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/69780

Title:	A Simulation-Based Experience in Learning Structures of Bayesian Networks to Represent How Students Learn Composite Concepts
Authors:	劉昭麟 Liu,Chao-Lin
Contributors:	資科系
Keywords:	Student modelling;learning patterns;bayesian networks;computer-assisted cognitive modelling;computer-assisted learning;machine learning
Date:	2008-09
Issue Date:	2014-09-11 10:44:18 (UTC+8)
Abstract:	Composite concepts result from the integration of multiple basic concepts by students to form high- level knowledge, so information about how students learn composite concepts can be used by instructors to facilitate students` learning, and the ways in which computational techniques can assist the study of the integration process are therefore intriguing for learning, cognition, and computer scientists. We provide an exploration of this problem using heuristic methods, search methods, and machine-learning techniques, while employing Bayesian networks as the language for representing the student models. Given experts` expectation about students and simulated students` responses to test items that were designed for the concepts, we try to find the Bayesian-network structure that best represents how students learn the composite concept of interest. The experiments were conducted with only simulated students. The accuracy achieved by the proposed classification methods spread over a wide range, depending on the quality of collected input evidence. We discuss the experimental procedures, compare the experimental results observed in certain experiments, provide two ways to analyse the influences of Q-matrices on the experimental results, and we hope that this simulation-based experience may contribute to the endeavours in mapping the human learning process.
Relation:	International Journal of Artificial Intelligence in Education,18(3), 237‒285
Data Type:	article
Appears in Collections:	[資訊科學系] 期刊論文

Files in This Item:

File	Description	Size	Format
237-285.pdf		4088Kb	Adobe PDF2	392	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...