Effects of a real-time adaptive strategy on learning computer networks through cognitive load analysis and scaffolding mechanism

• Main Authors: Huai-En Tu, 杜懷恩
• Other Authors: Nian-Shing Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/wra7be

碩士 === 國立中山大學 === 資訊管理學系研究所 === 104 === According to cognitive load theory (CLT), learners will use their cognitive resources to store in long-term memory in the form of cognitive schema when they have new learning task. Cognitive load reflects learners use cognitive resources in learning processes. Cognitive load generated overload when learners with learning difficulties in learning process is a very well established finding. Based on the two perspectives of learners’ thinking styles and brain-computer interface (BCI), this study proposes a real-time attention level monitoring mechanism in adaptive learning strategy to help learners decrease overloading cognitive load in learning process. Thinking style refers to personal preferences in learners’ abilities and cognitive experience to deal with learning task. Learners will integrate their cognitive resources and choose useful information to build up knowledge structure. In other words, thinking styles seem to be a knowledge acquisition path which they feel ease. This study designs two adaptive learning systems. Adaptive learning system gives learner real-time learning assistants according to learners’ leanings of thinking styles. This study designs a computer network learning activity for an experiment including 108 voluntary participants from at National Sun Yat-Sen University. The result showed that learners’ thinking styles and learning strategy have significant interactive effect. The finding suggests that learning strategy must consider learners’ thinking styles that can improve learning performance of constructing computer network knowledge. With the support of real-time attention level monitoring mechanism, adaptive learning system will give learners real-time learning assistants so that learners’ knowledge acquisition can be easily grounded and build up their own cognitive structure.