Research On The Development And Application Of The Predictive Model Of Junior High School Students’ Learning Performance Based On Machine Learning Algorithms

Educational information 2.0 requires the transformation of education and teaching to intelligence.Relying on big data and intelligent algorithms to improve academic development have become a hot spot in the field of educational technology research,and the effective prediction function application of machine learning is currently an important application direction of artificial intelligence in education and teaching.Under the background of the new college entrance examination,the role of junior high school academic development in K-12 education is further highlighted.At this stage,students’ thinking and knowledge levels have developed rapidly.Therefore,grasping the dynamics of students’ academic development during this period and timely and effective implementation of interventions have become the key to solving students’ academic development problems.This research focused on the development and application of a machine learning-based forecasting model for junior high school students’ academic performance,and aimed to provide ideas and technical solutions for solving the problems of junior high school students’ academic development.Based on the research problem to be solved,this research first constructed the element model that affects the student’s academic performance.Then developed the academic performance prediction model according to the element model design,and carried out the practical application and verification of this prediction model,Finally,the prediction model correlation analysis of relevant elements is carried out,and suggestions and countermeasures for intervening students’ academic development are obtained.The element model construction part of students’ academic performance takes into account the complexity of the factors that affect students’ learning results.The element model is constituted from three aspects: individual factors,environmental factors and psychological factors.In the learning performance prediction model part,a series of questionnaires were first designed and developed according to the element model,the data of each element was acquired,and the original data set was formed by combining the student’s previous test scores,and then the prediction model was developed,designed and verified according to the construction process of the student performance prediction model.In the correlation analysis part of the relevant elements,first the relevant elements are classified and divided into intervenable elements and non-intervention elements.Then,the correlation analysis between different elements and learning is carried out to generate an overview of the impact of each element on academic performance.Finally,the intervenable elements are divided into three categories: school,family,and individual.A sample proposes suggestions and countermeasures to provide a sample for the formulation of subsequent intervention strategies.