摘要
网络交互质量是影响网络教学效果的重要因素。已有的网络交互教学成效评价系统缺乏对学习过程的监控和对学习者的良好建模,评价大多凭借专家经验,评估的主观随意性大,评价结果与实际值存在一定的误差。贝叶斯网络有很强大的解决不确定问题的处理水平,是目前基于概率的不确定表达和智能推理方面最有效的理论模型之一。基于贝叶斯网络的网络交互教学成效评价系统,基于领域知识关系构建贝叶斯网络学生模型,并引入模糊数学变换方法对学生认知水平进行评估,能减少不正常因素的干扰,提高对学生认知能力评价的精确度,实现对网络教学交互的质量评估和个性化导学。
The quality of online interaction is an important factor affecting the quality of online instruction. The existing evaluation systems for the effectiveness of online interactive instruction are lack of learning process monitoring and sound modeling of the learners. The evaluation of these systems depends much on expert experiences and is therefore too subjective, causing differences between the evaluation results and the real situation. Bayesian network is one of the most effective theoretic models in probability-based uncertainty knowledge expression and inference, good at handling uncertainty problems. To apply the Bayesian network into the evaluation system of online interactive instruction, we established a Bayesian network student model based on domain knowledge. In this system, fuzzy mathematics method is introduced to evaluate the students＇ cognitive level, which reduces the interference of abnormal factors and improves the accuracy of the evaluation on students＇ cognitive level. Thereby the evaluation of the quality of online interactive instruction and individualized learning guidance are realized.
出处
《现代远程教育研究》
CSSCI
2012年第4期85-90,共6页
Modern Distance Education Research
基金
湖南省教育科学规划课题"基于智能技术的网络交互教学成效研究"(XJK011AXJ001)
关键词
网络交互质量
贝叶斯网络
评价系统
学生模型
模糊综合评价
the Quality of Online Interaction
Bayesian Network
Evaluation System, Student Moclel
Fuzzy Comprehensive Evaluation
