基于布鲁姆目标分类法的自适应学习系统研究与应用

Research and Application of Adaptive Learning System Based on Blooms Taxonomy

目前的网络在线学习主要提供固定学习内容给学习者,对学习材料与学习者之间在类型、种类和复杂性能否匹配的研究较少。采用改进的布鲁姆分类法(RBT)理论可以考虑学生的认知技能来设计自适应学习活动。为了实现这一目标,系统利用基于规则的决策模块中的模糊权重技术和RBT学习理论设计学习材料,针对学生的认知技能提供个性化的学习活动。将学习理论与个性化的学习方法相结合,可以增强以学生为主导的个性化学习,提高学生的学习积极性,提升知识的获取效率。

The current adaptive learning system mainly focuses on the presentation of domain knowledge,but there are few studies on the adaptation of the number,variety and complexity of students'learning activities.Based on the Revised Bloom Taxonomy(RBT)learning theory,students'cognitive skills can be considered to design adaptive learning activities.In order to achieve this goal,the system uses fuzzy weight technology in rule-based making decision module and RBT learning theory to design learning materials,and provides adaptive learning activities for students'cognitive skills.Combining learning theory with adaptive learning guidance can enhance student centered learning,promote student participation,and improve the efficiency of knowledge acquisition.