Research On Group Formation Strategy Of Online Learning From The Perspective Of Multi-objective Optimization

With the rapid development of Internet applications and the in-depth integration of emerging technologies such as artificial intelligence and big data with education,online learning has become a new trend in education applications in the context of the Internet era.Collaborative learning research under the network environment has become an important research field of educational technology.As an effective learning method,collaborative learning combined with online learning can fully cultivate learners' ability to solve problems cooperatively and adapt to the development needs of the information society.With development,collaborative learning research under the network environment has become an important research field of educational technology.The learning group is the core component of collaborative learning,and the scientific and reasonable formation of online learning groups is also a prerequisite for online collaborative learning.How to construct an online learning group model based on the multi-dimensional characteristics of learners,and to design a learning group formation strategy that meets the recognition of learners and meets the needs of teaching is the current research hotspot and difficulty.In response to the above problems,this research has mainly done the following work.Firstly,sort out and analyze the current research status of collaborative learning,learning group formation and online learning group formation technology at home and abroad,and provide theoretical support for subsequent research work.Secondly,according to the learning community theory,the basic viewpoints of connectivity theory and learning style theory,in-depth exploration of the classification methods and grouping modes of online learning groups,summarize and refine the factors that affect the formation of learning groups and their internal logical relationships,and build an online learning group formation model from the perspective of multi-objective optimization.Thirdly,the problem of online learning group formation is abstracted into a multi-objective optimization problem,the complementarity and intimacy of learners within the online learning group are analyzed,genetic algorithm and multi-objective particle swarm algorithm are combined,and the online learning group formation algorithm is improved and optimized.Finally,a strategy for online learning group formation under a multi-objective optimization perspective is proposed,that is,learners are divided into a number of optimal online learning groups that meet the teaching requirements and recognized by the learners,and match the overall requirements of the learning group with the willingness of learners in the learning group to be intimate according to the internal demand of the proposed online learning group formation algorithm,the model is solved.This study uses benchmark test functions to verify the effectiveness of the online learning group formation algorithm,and validates the operating efficiency of the proposed strategy through a controlled experiment of random grouping and singletarget grouping strategies,and designing an online learning case Webpage Design and Production course description the actual application process of the proposed strategy.The experimental results show that this research solves the problem of online learning group formation with the idea of multi-objective optimization,explores the internal logical relationship between learner characteristics,and designs online learning group formation strategies that meet the requirements of collaborative learning and are highly recognized by learners,thereby improving learning the online collaborative learning experience and learning effects of online collaboration provide a reference for intelligent computing to solve the problem of learning group formation.