Reseaech On Online Collaboration Grouping Strategy Based On Social Computing

The information age emphasizes that talents must have the ability to collaborate.Collaboration skills such as collaboratively asking questions,collaboratively analyzing questions,collaboratively searching for existing answers and screening answers,and collaboratively proposing new solutions are core skills necessary for 21st-century talents.With the increasing emphasis on collaboration capabilities and the rapid development of online learning spaces,online collaborative learning models have received widespread attention.The deep interaction between learners is the core element of online collaborative learning mode to exert its application value.However,at the present stage,there are many problems in the practice of learning,such as low enthusiasm for discussion,low quality of interaction and low number of learners.These problems seriously restrict the value of online collaborative learning models and reduce the performance of online collaborative learning models.In order to solve the above problems,it is necessary to integrate multidisciplinary knowledge such as pedagogy theory and computer network technology,and social computing provides this support.The research proposes an online collaboration grouping strategy based on social computing.This strategy is based on the traditional heterogeneous grouping strategy,adding the role of group bridges,with the hope that group bridges will bring heterogeneous group members to participate fully in discussions and exchanges.This strategy first introduces the role of a group bridge,which is a learner in the learning group who has more information transmission capabilities than other learners.After collecting the original interaction matrix,the group bridge role in the group is identified through two complementary technical means,namely,the intermediate centrality analysis of the points in the social calculation and the role analysis.Then,collect the knowledge base information and learner characteristic information of other learners except the group bridge,and use algorithm modules with cluster analysis in social computing as the main technical means to identify heterogeneous group members.Finally,group bridges and heterogeneous group members are formed into study groups.In order to verify the use effect of this grouping strategy,the research design is based on the grouping strategy and applied to teaching practice.The instructional design includes three links before,during and after the course.In this study,33 learners from a normal university who participated in online collaborative learning activities were selected as the research objects,and an experimental group and a control group were set up.The experimental group contains 16 people and uses the teaching design based on this grouping strategy for teaching;the control group contains 17 people and uses random grouping for teaching.The experimental group and the control group both took "Design and Development of MicroLessons" as the teaching content,and carried out an 8-week experimental activity.The teachers in the class were the same teacher.The social network analysis,interactive content analysis,statistical analysis,and interview methods were used to evaluate the use of the grouping strategy and the impact on the deep interaction between learners.The research results show that: first,the relationship between learners using this grouping strategy is higher than that of random grouping.Secondly,the level of knowledge construction in this grouping strategy is significantly higher than that of random grouping,and the irrelevant content is significantly less than that of random grouping.At the same time,the learning performance of learners using this grouping strategy is significantly higher than that of learners using random grouping.After that,learners are more satisfied with the grouping strategy.Finally,the teachers of the class are more satisfied with the grouping strategy,and believe that the grouping strategy stimulates the enthusiasm of learners to participate in the deep interaction between learners.The above results show that the grouping strategy can effectively promote deep interaction and improve the quality and efficiency of learning.