Deep Learning Student Evaluation Using Multidimensional AI Data
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Solution Overview
Problem
Current AI technologies in classroom environments are limited to single-technical and single-time space identification, leading to resource waste and redundancy, failing to fully utilize their potential in intelligent education.
Innovation Solution
A method and system for comprehensive student performance evaluation using a deep learning network that processes AI identification data across multiple dimensions, incorporating simulated data generation and training algorithms to enhance prediction accuracy and enable real-time evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If single-technical and single time-space identification is used, then resource waste and redundancy occur, but the system complexity remains low
Solution Approach 1:
The patent combines multiple single-technical identification systems (emotion recognition, gesture recognition, sight line recognition) into a unified multidimensional identification platform. These previously separate systems that caused resource waste are merged into a single integrated system that shares common infrastructure, thereby reducing redundancy while maintaining full functionality.
Solution Approach 2:
The identification system is designed to perform multiple functions simultaneously - it can identify emotions, gestures, and sight lines across multiple students at the same time. This multi-functional approach eliminates the need for separate dedicated systems for each identification type, reducing overall resource consumption and hardware redundancy.
2Measurement precision
If comprehensive performance evaluation across multiple dimensions is implemented, then evaluation accuracy improves, but data processing complexity increases
Solution Approach 1:
The comprehensive evaluation system is segmented into distinct modular components: emotion recognition module, gesture recognition module, sight line recognition module, and performance analysis module. Each module processes specific types of data independently and outputs structured results that are then integrated. This segmentation allows complex multidimensional evaluation to be performed through coordinated simple modules, managing complexity while maintaining comprehensive accuracy.
3Adaptability or versatility
If AI identification technology is applied to multiple students simultaneously, then evaluation comprehensiveness improves, but computational load increases
Solution Approach 1:
The system implements periodic batch processing of student identification data rather than continuous real-time processing for all students simultaneously. Data from multiple students is collected over a defined period, then processed in batches using the deep learning network. This periodic approach maintains comprehensive evaluation capability while significantly reducing instantaneous computational load and energy consumption.
Data Source
AI summary
The present disclosure provides a student performance evaluation method and system based on artificial intelligence (AI) identification data, and relates to the field of intelligent education. A lightweight network model suitable for student performance evaluation takes the AI identification data as an input and evaluation results as an output. A training data generation algorithm is provided, and multidimensional AI identification data and labels are uniformly processed into training data suitable for the network model through the above algorithm, which can solve the problems that dimensions between any AI identification data and various labels are not uniform, and original data cannot meet training of a multidimensional and cross-time prediction model. A simulated data generation algorithm and a simulated label generation algorithm are provided, and simulated training data is generated using these algorithms in conjunction with the training data generation algorithm.