A Study of a Data-Driven Dynamic Evaluation Model for Self-Regulated Learning
Accurate evaluation of learners'self-regulated learning process is a necessary prerequisite for the realization of pedagogical interventions.However,most of the existing evaluation methods are summative and insufficient in real time.For this reason,the study proposes a data-driven dynamic evaluation model of self-regulated learning.The learning process is temporalized in terms of learning tasks,and the mapping of generative data to self-regulated learning states is constructed in the learning process.The results of the study show that:(1)Generative data is a powerful factor in evaluating learners'self-regulated learning state,and the model has high validity.(2)The learners'self-regulated learning state shows a trend of differential changes in different evaluation dimensions,in which the dimensions of task analysis,self-motivated beliefs and self-observation tend to be stable,while the three dimensions of self-control,self-judgement,and self-response show obvious fluctuating changes.(3)The high and low performance groups show significant differences in the three evaluation dimensions of self-motivated beliefs,self-control and self-reaction,while there are no statistical differences in the three dimensions of task analysis,self-observation and self-judgment.
万方数据
维普
