Research On The Influence Of Grouping Strategy Based On Genetic Algorithm On Knowledge Building

Knowledge building is a research hotspot of Computer-Supported Collaborative Learning,that is realized by collaborative learning.Grouping is an important step to the success of collaborative learning,and it is also a factor affecting knowledge building.Through literature research,it is found that there are many researches on strategies and methods to promote knowledge building in the field of knowledge building,therefore it is meaningful to study how to optimize the grouping and advance collaboration so as to promote students' knowledge building.Genetic algorithm is an adaptive and global probability search algorithm,which has been used for grouping and proved to be able to optimize collaboration performance.The research will explore the influence of the grouping strategy of genetic algorithm on the knowledge building.The research adopts the experimental research method,relying on the course of TV electroacoustic system,and carries out knowledge building activities on the platform of "Shuke".First of all,according to the theory of knowledge building,analyzing the elements of learning activities,and combining with the course content,the elements of knowledge building activities are determined and knowledge building activities are designed;secondly,according to the process of genetic algorithm and a penalty function scheme based on genetic algorithm,after the students' prior knowledge level,students' role and close centrality are determined as the grouping basis,carrying out many experiments with MATLAB software realized the genetic algorithm grouping code adopted by the research.Thirdly,the sophomores of a university were randomly grouped to form the experimental group and the control group.The two groups of students used different grouping strategies when they carried out the same knowledge building activities,the experimental group used the genetic algorithmgrouping strategy,and the control group used the free combination of students grouping strategy.Finally,through the collection of students' discourse content on the platform,interactive dialogue behavior data,and personal and group knowledge achievements after practice,utilizing the evaluation scheme proposed in the knowledge building activities,in the process dimension,adopting tools of the interaction analysis model for examining social construction of knowledge proposed by Gunawardena and the coding table of view improvement and development,and social network analysis method to evaluate knowledge building;in the result dimension,adopting the Hough concept map analysis model and the design scheme evaluation table to evaluate knowledge building.The research shows that in the process dimension,the grouping strategy of genetic algorithm is conducive to the generation of high-level knowledge building discourse and the knowledge building of students in depth;it is more beneficial to the generation of improvement and development views,and to the continuous improvement of public views;at the same time,individuals can make more active contributions to public knowledge and realize the transformation of individual knowledge to public knowledge.In the result dimension,under the grouping strategy of genetic algorithm,individuals and groups have a better understanding of knowledge,a more abundant understanding of related topics and a perfect knowledge structure through knowledge building.Through the evaluation of process and result dimensions,it is proved that the grouping strategy of genetic algorithm has a more positive impact on the knowledge building of college students,and can promote the knowledge building of students.Through the comprehensive evaluation of process and result dimensions,it is shown that the grouping strategy of genetic algorithm can improve the quality of knowledge building process,spur the occurrence of students' high-level knowledge building,so as to promote the mastery of students' individual knowledge and optimize the team collaborative performance.