An Affective Recognition-Based Architecture for Intelligent Learning Environments
It is now widely accepted that intelligent learning environments are expected to care about both learners and tutors, and to have a good understanding of the variety of learning contexts. The key research question now is how to tackle the complex issues related to building learning systems that care, ranging from representing knowledge and context to modeling social, cognitive, metacognitive, and affective dimensions. In allusion to the absence of affective dimensions in traditional Intelligent Learning Environment (ILE), an improved architecture based on affective recognition is proposed in this paper, we add two components as emotion model and learning assessment model in the classical architecture proposed by Adriana da Silva (2008),which can get, recognize and analyse affective information of students learning performance, then affective stimulation and affective tutoring is implementation according to different students learning emotion, and then pass this information to affective information processing model. Pedagogical strategies can change accordingly to learners emotion, appropriately conduction of emotional motivation can help to achieve the best quality of learning.
intelligent learning environments(ILE) ontology semantic web affective recognition
YIN Gui-Mei GUO Guang-Xing
Computer Science Department Tai Yuan Normal University, TYNU Shan Xi Tai Yuan, China Urbanism and Tourism Academy Tai Yuan Normal University, TYNU Shan Xi Tai Yuan, China
国际会议
太原
英文
237-239
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）