Construction of a Stacking Ensemble Learning Model for Academic Performance in Digital Media Majors

Given the limitations of traditional single prediction models in dealing with complex influencing factors on academic performance in universities, this study proposes an academic performance prediction method based on Stacking multi model fusion. Taking college students majoring in digital media as the research object, a learning engagement measurement scale is introduced to provide targeted data support for model construction, and the principle of Stacking ensemble learning and its advantages in improving model generalization ability and stability are systematically explained. Provide a detailed description of the construction process for Stacking ensemble learning models. Select candidate algorithms from random forest, XGBoost, and KNN machine learning algorithms in the selection of basic learners; Using linear regression algorithm as a meta learner, optimizing model parameters through cross validation strategy; At the same time, strict preprocessing is carried out on the collected data, and raverse all combinations of base classifier algorithms for ensemble training, and construct a Stacking ensemble classifier. In the experimental stage, based on students' grades in digital media courses, the effectiveness of the model is evaluated by comparing the predictive performance of Stacking ensemble classifiers and single classifiers. The experimental results show that the Stacking ensemble classifier performs excellently in prediction accuracy and robustness, significantly better than a single classifier. The R² Score has increased by approximately 10.42% to 16.82%. This model can objectively and accurately reflect students' learning status, providing scientific basis for blended course teaching design, digital media professional teaching management strategy formulation, and personalized guidance for students' academic careers.