Student Performance Prediction Using Behavior Time Series Reconstruction
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Solution Overview
Problem
Current student performance prediction methods rely heavily on subjective teacher assessments, which are time-consuming and inconsistent, necessitating an objective and accurate method for predicting student performance.
Innovation Solution
A method involving obtaining learning behavior data, performing feature fusion, and using a pre-trained feature reconstruction network and student performance prediction model to predict student performance objectively and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If subjective teacher assessments are used for student performance prediction, then the method can be implemented with existing resources, but the prediction process becomes time-consuming and inconsistent
Solution Approach 1:
The patent replaces the mechanical system of manual teacher assessment with an automated computer-based prediction system. The system uses machine learning models to process student data and generate performance predictions, eliminating the need for time-consuming manual evaluations while maintaining ease of implementation through automated workflows.
Solution Approach 2:
The prediction system performs self-service by automatically collecting student data, processing it through the machine learning model, and generating predictions without requiring manual intervention. The system serves itself by automating the entire prediction pipeline, from data collection to result generation, thereby improving efficiency while remaining easy to implement.
2Ease of manufacture
If subjective teacher assessments are used for student performance prediction, then the method can be implemented with existing resources, but the assessment results become inconsistent across different teachers
Solution Approach 1:
The patent replaces the mechanical system of manual teacher assessment with an automated computer-based prediction system. The system uses machine learning models to process student data and generate performance predictions, eliminating the need for time-consuming manual evaluations while maintaining ease of implementation through automated workflows.
Solution Approach 2:
The patent changes the parameters of the assessment system by transitioning from subjective human judgment to objective algorithmic processing. The machine learning model uses standardized parameters and features to consistently evaluate student performance, ensuring reliability and consistency across different implementations while remaining easy to deploy.
3Measurement precision
If detailed feature fusion and reconstruction networks are used, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex prediction system into distinct modules: feature extraction, feature fusion, and reconstruction network. Each module handles specific aspects of the prediction process, allowing for improved accuracy through detailed processing while managing complexity through modular architecture. This segmentation enables the system to achieve high measurement precision without overwhelming system complexity.
Data Source
AI summary
A student performance prediction method, apparatus, electronic device, and computer-readable storage medium, wherein the student performance prediction method includes: obtaining learning behavior data corresponding to different target behaviors among multiple behavior categories of a student within a preset time period; and aggregating the learning behavior data corresponding to the different target behaviors in a preset time unit, and performing feature fusion on the aggregated data separately for each behavior category to obtain a category feature set, determining the multiple category feature sets organized in chronological order as a category feature time series set, inputting the category feature time series set into a pre-trained feature reconstruction network to obtain a reconstructed time series set, and inputting the reconstructed time series set into a student performance prediction model to obtain a performance prediction result for the student. The above student performance prediction method can objectively and efficiently predict the student performance.