由于认知负荷作为学习者学习过程中认知系统所产生的负载，对学习者的学习状态有重要影响，并且目前已有的学习者模型中缺乏对学习者认知负荷的研究。为此以教育部教育信息化技术标准委员会提出的CELTS-11(China E-Learning Technology Standardization-11)为基础，将认知负荷作为一个维度融入学习者模型，构建了静态与动态信息相结合的LMICL(Learner Model Incorporating Cognitive Load)。然后，以自适应学习系统为依托，将未融合认知负荷的学习者模型的数据和LMICL的数据分别作为推荐学习资源的依据，产生了两种不同的学习资源推荐结果，并随机选取两个班级的学习者在该系统中进行学习，最后从学习者的学习成绩、认知负荷结果和满意度3个指标对LMICL的效果进行验证。结果表明，基于LMICL的推荐学习效果强于未融合认知负荷的学习者模型。
Research on Construction and Recommendation of Learner Model Integrating Cognitive Load
The current learner model lacks exploration of this dimension of cognitive load,which,as a load generated by the cognitive system during the learning process,has a significant impact on the learning state of learners.Basedon the CELTS-11(China E-Learning Technology Standardization-11)proposed by the China E-Learning Technology Standardization Committee,cognitive load is integrated into the learner model as a dimension,and an LMICL(Learner Model Incorporating Cognitive Load)combining static and dynamic information is constructed.Afterwards,relying on an adaptive learning system,the data of the unmixed cognitive load learner model and the LMICL data were used as the basis for recommending learning resources,resulting in two different learning resource recommendation results.Two classes of learners were randomly selected to learn system,and then their academic performance.The results of cognitive load and satisfaction were used to validate the effectiveness of LMICL,and it was found that the recommendation learning effect based on LMICL was better than that of the learner model without integrating cognitive load.
learner modelcognitive loadlearner model incorporating cognitive load(LMICL)
万方数据
维普
