KEYWORDS: Feature extraction, Machine learning, Performance modeling, Deep learning, Neural networks, Data modeling, Ablation, Time series analysis, Matrices
To address the cold start problem in academic performance prediction with student behavioral sequence, we propose a bilateral branch structured neural network DNN-CBLM. DNN-CBLM combines behavioral sequences and students' personal information covariates for jointly time series analysis. The two network branches are designed for feature extraction of student click behavioral time series data and students' background covariates, respectively. The extract features are finally aggregated for performance prediction. In the comparison experiments, it is demonstrated that DNNCBLM has better prediction performance than other models.
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