Research On CAUX-Assisted User Fragmented Learning Behavior

The development of mobile internet applications has brought about the fragmentation of learning.There are various user experience problems in people’s fragmented learning process.For example,it is difficult to store relevant pieces of knowledge in a centralized manner to facilitate future review and use.All these problems need to be studied and resolved.When studying the user experience of fragmented learning,traditional methods cannot fully capture the user’s fragmented learning behavior in real situations 7*24,and the granularity of the situational information obtained is relatively coarse,which cannot well meet the research needs.Therefore,it is worth exploring whether automated user data collection tools can be used to assist user experience engineers in the research of fragmented learning user experience.The author of this article attempts to explore the way to solve the problem with the help of a context-aware user experience research tool CAUX.The research work mainly includes four aspects.(1)Based on the existing fragmented learning model,the user’s fragmented learning,especially the knowledge preservation behavior in it,is analyzed by the actual investigation method,and the task model of the knowledge preservation behavior is established.(2)With the support of the knowledge preservation behavior task model,a user data collection plan was developed,and the CAUX tool was used to collect the knowledge preservation behavior data of 17 users within 73440 hours in a context-aware manner,as well as the corresponding system status and environmental status and other contextual data,Using data visualization combined with manual analysis to analyze and discover the rules of user knowledge preservation behavior and existing user experience problems.(3)In data analysis,design and realize the automatic identification method of specific subtasks in the process of user knowledge preservation,as well as the automatic elimination method of non-target data,improve the efficiency of data analysis,reduce labor costs,and verify its effectiveness through actual application cases.And further expand the discovered behavioral rules of user knowledge preservation and user experience problems.(4)Perform a hierarchical analysis of the findings obtained in the above research,select five representative user experience problems,analyze the product experience design defects that caused the problems one by one,propose corresponding design improvements,and establish Axure high-fidelity prototypes of the new and old design solutions,Through usability testing to verify the design improvement effect.The research results show that the use of automatic user data collection and analysis tools to study fragmented learning behavior can break through the limitations of traditional methods,greatly expand the understanding of related phenomena and problems,and strongly support the improvement of fragmented learning user experience.