MOOCs环境下个性化学习需求预测建模与仿真——系统动力学的视角

Modeling and Simulation of Personalized Learning Needs Prediction under MOOCs Environment: the Perspective of System Dynamics

MOOCs环境下,基于学习者前期学习表现进行个性化学习需求预测,能够进一步优化学习体验,提高学习者课程参与度。研究以学习者的个性化学习需求为研究内容,以系统动力学为指导方法,并交叉使用层次分析法和非线性回归分析确定变量间的数量关系,建立个性化学习需求预测模型。最后结合两门不同语种课程数据进行模拟仿真分析,对学习者各方面学习需求变化状况和引起学习需求变化的高杠杆因素进行探索与验证。研究结果表明:所构建的预测模型包括内容、资源、过程和评价四个需求子系统,涵盖3个状态变量、4个流率变量、23个辅助变量和20个常量,能够对学习者的个性化学习需求进行准确预测;内容难度需求和评价标准需求是个性化学习需求变化的主要体现,这两方面分别与学习者知识总量和学习投入总量呈正向显著相关;学习兴趣、需求满足程度以及课程目标是需求预测中需要关注的高杠杆因素;不同的课程中,学习者个性化学习需求变化的主要体现与需要关注的高杠杆因素相同,但高杠杆因素的影响程度会随课程不同而有所变化。

The prediction of personalized learning needs based on learner＇s early learning performance can further optimize learner＇s learning experience and improve his participation in curriculum under the MOOCs environment. This study takes the learner＇s personalized learning needs as the research content, adopts the system dynamics as the guiding method, and cross uses the analytic hierarchy process and nonlinear regression analysis to determine the quantitative relationship between variables to establish a prediction model for personalized learning needs. Finally, the simulation analysis is carried out with the data of two courses in different languages, the changes of learners＇ learning needs in various aspects and the highly leveraged factors causing the changes of learning needs are explored and verified. The research results show that the prediction model consists of four subsystems including content, resources, process and evaluation. The model covers three state variables, four flow variables, twenty-three auxiliary variables and twenty constants, which can accurately predict the personalized learning needs of learners. The demand of content difficulty and evaluation standard are the main embodiment of personalized learning needs, which are positively correlated with the total amount of knowledge and the total amount of learning input of learners. Learning interest, demand satisfaction, and curriculum objectives are the high leverage factors that need to be paid attention to in prediction. In different courses, the main embodiment of the changes of learners＇ personalized learning needs and the high leverage factors that need to be paid attention to are the same. However, there will be a change with the high leverage factors＇ influence degree in different courses.