Research On Self-Regulated Behavior And Guiding Strategies Of MOOC Learners

“Ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for the public "(SDG4)is the development goal of global education proposed by the United Nations in the 21 st century.Promoting the open education resources movement and implementing online education are the main ways to achieve this goal.Massive Open Online Courses(MOOCs)with many advantages such as unrestricted time and space,high accessibility and low cost attracts learners with diverse motivations and needs,and becomes an important part of classroom teaching and lifelong learning.However,due to the free learning environment and the lack of learning supervision mechanism,MOOCs poses new challenges to learners’ selfregulated learning ability and teaching support services,which could lead to poor learning experience and learning effect easily.The research shows that learning analysis is an effective way to realize self-regulated learning evaluation and support,which can facilitate learners to better online learning.The research on self-regulated learning of MOOC learners is a breakthrough to improve learning experience and learning effect under the new educational environment,and it has rapidly become the current research hotspot.Although a lot of relevant studies have emerged at home and abroad,the research on MOOC learners’ self-regulated behaviors and guidance strategies still faces the following challenges in practical application: Firstly,at the evaluation level of the learning experience,the use of a single learning behavior dimension to describe the learning experience may lead to a one-sided interpretation of the learning experience and the low evaluation accuracy.Secondly,in terms of self-regulated learning behavior and learning effect analysis,most current researches focus on the correlation between self-regulated learning behavior and learning effect.It lacks the internal mechanism analysis based on the proprietary model,which leads to the unreliability of the analysis results and fails to provide systematic decision support for teaching and learning.Thirdly,in terms of self-regulated learning behavior guidance strategies,although the learning analysis provides an evaluation method for MOOC learners’ self-regulated learning,the discussion on how to effectively support their self-regulated learning is insufficient and needs to be further explored.Aiming at the challenges faced by the research on the self-regulated behaviors and guiding strategies of MOOC learners,this essay systematically studies the self-regulated learning behaviors and guiding strategies of MOOC learners by establishing a self-regulated behavior model and an analytical framework,so as to deepen the breadth and depth of the research on the online learning behaviors and enhance support for improving the online learning experience and learning effects.The main work and contributions of this essay are as follows:(1)A self-regulated learning behavior model of MOOC learners has been constructed.This model includes four modules: self-regulated process,learning behavior domain,learning behavior regulation and selfregulated strategy.It clarifies the internal correlation and basic structure among MOOC learners’ self-regulated behavior,regulation process and regulation strategy,which is conducive to ensuring learners’ online learning experience and effects.(2)This essay studies the self-regulated behavior of MOOC learners’ learning time.First of all,the differential stationary test is carried out on the learner’s behavior time series to establish the learner’s behavior rhythm during the course period.Next,the analysis of variance and t test are used to analyze the characteristics of learning time adjustment of learners’ resource interaction behavior.Finally,the characteristics and differences of learning time regulation behavior of learners with different grades were studied.It is found that the time series of MOOC learners’ online learning behavior presents non-stationary characteristics,and there is a three-step rhythm in the allocation of learners’ learning time during the course period.There are significant differences in resource interaction behavior and learning time allocation among learners of different grades,and the behavior frequency and time investment of learners in the excellent group are maintained at a high level.(3)The self-regulated behavior of MOOC learners’ learning paths has been studied.Firstly,this essay studies the correlation between learners’ self-regulated behavior and academic performance through Pearson correlation analysis.Then Ward and K-means clustering algorithm and variance analysis are used to study the behavioral activity and achievement distribution of learners of different groups.Finally,The LSA analysis is used to reveal the differences in learning path regulation behavior of different achievement groups.The most relevant behavior for learning outcomes is login behavior,followed by the participation in learning activities.Students’ participation has a significant impact on their learning outcomes.The high-achieving group(high and medium)showed higher activity levels in performance-driven and goal-driven activities.Low-achieving groups may have some difficulties in self-regulated learning.(4)This essay researches the attentional self-regulated behavior of MOOC learners.First of all,based on the meta-attention theory and the existing research results,six research hypotheses are formed,and a research model of MOOC learners’ self-regulation of attention is constructed accordingly.Then,using attention measurement tools and the existing research results to compile the “MOOC Learners’ Attention Self-regulation Questionnaire”.After the reliability and validity test,1332 valid sample data are obtained by stratified sampling method.Finally,the hypotheses are verified and the path analysis is carried out by structural equation model(PLS-SEM).The result shows that MOOC learners have more common attention problems.The media multitasking self-efficacy and self-regulated strategies have significant positive effects on learning satisfaction.The media multitasking self-efficacy affects the process of learning satisfaction,and it would be affected by the multiple mediating effects of perceived attention problem and self-regulated strategies.Gender,age and online learning experience have no moderating effect on the path of learning satisfaction.(5)The self-regulated effect of MOOC learners is studied.Firstly,the behavior patterns of MOOC completers are summarized by K-means clustering algorithm.Then,the input and output index system of MOOC learners’ self-regulated learning behavior is constructed.Lastly,the data envelopment analysis(DEA)is used to measure the learning efficiency of MOOC learners and the differences between groups.The result shows that the behavior patterns of MOOC completers are significantly different,and the diligent harvesters are higher than those who punch the clock in the two dimensions of learning input and learning output.MOOC completers have significant differences in learning efficiency and contribution rate of input,and diligent learners are not always the best in learning efficiency.The effective ratio of pure technical efficiency of the punch-card experiencers is higher,so there is much space for improvement.The forum reply frequency of learners has the highest contribution rate to their learning efficiency.(6)This essay puts forward the self-regulated behavior guidance strategies of MOOC learners.Based on the online learning environment theory,self-regulated learning theory and social learning theory,this essay integrates the findings of the research and focuses on improving the learning experience of MOOC learners and their self-regulated learning abilities.According to three dimensions of teaching design,process management and environmental support,this essay puts forward the self-regulated behavior guidance strategies for MOOC learners from six aspects: stage characteristics of learning engagement,leading activity design,evaluation project design,learning situation monitoring,reward structure,environmental organization.