The Quantitative Research On Centrality And Difficulty Of Education Resource Knowledge Unit

"Learning trek" and "Cognitive overload" is the important influencing factors for e-learning efficiency. "Learning trek" refers to the learners do not know what they want when they face all kinds of education resources in e-learning, which make learning become aimless roaming. "Cognitive overload" refers to the learners received knowledge exceeds the processing power of their knowledge so that knowledge are not fully applied.The core technical of research to overcome "Learning trek" and "Cognitive overload" in e-learning is how to distinguish the centrality and difficulty of knowledge unit in the massive growth of educational resources, in this way, users can get accurate navigation learning path, and it is helpful for users learning.According to those problems, this thesis quantitative research the centrality and difficulty of knowledge unit in education resources, the specific work and innovation are as follows:1)The build of learning navigation path. We build learning navigation path based on cognitive dependencies between knowledge unit and visualized show it by knowledge map, and also we analyzes how the cognition dependencies between knowledge unit in education resources affect learning efficiency, and quantitative the cognitive dependencies between knowledge unit.2)The algorithm designing of the centrality of knowledge unit. The centrality of knowledge unit is the influence of knowledge unit in the knowledge hierarchy. We weighted for knowledge unit based on the structure characteristics of knowledge map. We built learning model by absorption state Markov chain, and using the weight of knowledge unit to quantify the learning transfer, at last, this thesis through predict learning path in the process of total path to measure the centrality of the knowledge unit.3)The algorithm designing of the difficulty of knowledge unit. Our difficulty algorithm complied with the laws of human cognitive learning, this thesis mainly from content difficulty and statistical difficulty to consider. Content difficulty considering the depth and breadth of knowledge unit, we use hierarchical level to measure, statistical difficulty uses the feedback of testing to calculate, at the same time, in order to prevent level difficulty may lead to mismatch difficulty, so we using interval scale to calculation difficulty.4)Simulation algorithm. This thesis simulation algorithm by MATLAB, we compared with the existing methods to verify the efficiency of our algorithm, and according to the experimental results to improve and perfect the algorithm.The experiment results show that, the results of this thesis can be effectively applied to quantitative the centrality and difficulty of knowledge unit in education resources, it is positive reference for personalized learning and navigation learning path.