| Since 2013,online education platforms represented by MOOC and Taobao University have developed strongly.As of June 2021,the number of Internet users in China has reached 1.011 billion,of which the number of online education users has reached 325 million,accounting for 32.1%of the total number of Internet users.During the epidemic period,1454 colleges and universities across the country carried out online teaching,with a total of 17.75 million college students participating,with a total of 2.3 billion person times.The traditional education market realized the integration of online and offline due to digital empowerment.Fragment chemistry learning is becoming popular in the network era,and has increasingly developed into one of the important learning methods for online learners.It is also increasingly closely combined with college teaching practice.With the development of digital education products and services of mobile terminals,smart phones have shown their advantages in various learning and education scenes,changing the form and speed of knowledge transmission.Using mobile phones to carry out fragmented learning is becoming more and more popular among college students.Using the method of literature review,this paper summarizes the development status of fragmented learning research,the influencing factors of fragmented learning effect and optimization strategies.Taking the fragmented learning activities carried out by college students using mobile phones as the research object,this paper divides fragmented learning into three types:interaction based,content-based and scenario-based fragmented learning,This paper summarizes the typical characteristics of fragmented learning,including the miniaturization and networking of learning content,the autonomy of learning subjects,the diversification of resource construction and the flexibility and interaction of learning process,and expounds the differences and relations between fragmented learning and superficial learning,systematic learning and micro class.Based on the correct understanding of fragmented learning,this paper takes the learning process model proposed by Biggs as the theoretical basis,selects the fragmented learning in mobile learning platform,we media platform and financial media platform as typical cases,and explores the changes of learners,learning situation,learning strategy and learning effect compared with the traditional classroom,Combined with the theoretical derivation and optimization of Biggs’s learning process model,this paper puts forward the theoretical model and research hypothesis of the influencing factors of fragmented learning effect based on mobile terminal,and explores the influence of student factors,learning situation factors and learning strategy factors on fragmented learning effect.The results show that the information ability and time management ability in students’ factors have a significant positive impact on the effect of fragmented learning,the social interaction and network resource quality in learning situation factors have a significant positive impact on the effect of fragmented learning,and the deep learning strategy also has a significant positive impact on the effect of fragmented learning.In the process of information ability,time management ability and social interaction affecting the learning effect,deep learning strategy plays a mediating effect to a certain extent.How to improve the effect of College Students’ fragmented learning is an important topic in the era of e-learning.By uncovering the "black box" of the influencing factors of fragmented learning effect,this paper puts forward the improvement strategies of fragmented learning effect,which is conducive to the education subject,platform and policy makers to confirm the connotation of fragmented learning and follow the development law of fragmented learning,Improve the network fragmented learning environment.On the basis of a correct understanding of fragmented learning,it is easier for learners to master fragmented learning methods and improve their learning effect and learning efficiency. |