GAN-Based Test Paper Generation for Difficulty Balancing
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Solution Overview
Problem
Conventional methods for generating test papers are time-consuming and inefficient, as teachers spend significant time crafting questions that balance difficulty, scope, and duration while avoiding repetition, leading to a lack of effective differentiation in assessing student learning abilities.
Innovation Solution
An electronic system and method using a graphical user interface (GUI) and a generative adversarial network (GAN) module to generate test papers based on user inputs, allowing for efficient customization of question number, difficulty, type, and time, with options for preview and adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If teachers manually design test papers, then the test paper quality and differentiation capability are improved, but the time consumption and work efficiency deteriorate
Solution Approach 1:
The system enables automated test paper generation where the computer system itself performs the design and assembly tasks that previously required teacher intervention. The generation module automatically creates test papers based on stored question banks and specified parameters, eliminating the need for teachers to manually craft questions while maintaining quality through algorithmic optimization.
Solution Approach 2:
The patent replaces the manual mechanical process of test paper design with an automated computational system. The generation module uses computer algorithms to select, assemble, and format test questions based on educational parameters, substituting the teacher's manual cognitive work with automated information processing.
2Reliability
If teachers manually design test papers, then the differentiation capability is improved, but the productivity and output efficiency deteriorate
Solution Approach 1:
The system automatically generates multiple test paper variants with different difficulty levels and question compositions. The generation module can produce numerous differentiated test papers in parallel, each tailored to different student abilities, without requiring proportional increases in teacher workload.
Solution Approach 2:
The system pre-stores a comprehensive question bank with detailed metadata including difficulty levels, knowledge points, and question types. This preliminary organization of questions enables rapid assembly of differentiated test papers when needed, eliminating the need for teachers to create differentiation options from scratch each time.
3Reliability
If teachers design test papers avoiding previous test questions, then the assessment validity is improved, but the time consumption and complexity deteriorate
Solution Approach 1:
The system uses automated algorithms to manage the complexity of avoiding question repetition. The generation module tracks question usage history and automatically selects questions that have not been recently used, handling the complex tracking and selection logic computationally rather than requiring manual teacher oversight.
Solution Approach 2:
The system introduces an intermediate management layer between the question bank and test paper generation. The question bank is organized with metadata and tracking mechanisms that mediate the selection process, automatically identifying suitable questions that meet differentiation requirements without teacher intervention.
4Reliability
If teachers consider multiple factors in test paper design, then the test paper quality is improved, but the ease of operation and simplicity deteriorate
Solution Approach 1:
The system automatically handles the complex coordination of multiple design factors including question selection, difficulty balancing, and time allocation. The generation module self-manages these parameters based on user inputs, eliminating the need for teachers to manually balance each factor while still achieving high-quality results.
Solution Approach 2:
The system accepts high-level parameter inputs from teachers (such as total time, difficulty level, number of questions) and automatically translates these into detailed test paper compositions. This parameter-driven approach simplifies operation by allowing teachers to specify outcomes rather than manage the complex process of achieving them.
Data Source
AI summary
An electronic system and a method for generating a test paper are provided. The electronic system includes an electronic device. The electronic device includes an input device, a display device, a first storage medium, and a first processing device. The method includes: executing a generating test paper program and providing a graphical user interface; obtaining a user requirement according to a first user operation on the graphical user interface; generating the test paper according to the user requirement; and displaying a preview of the test paper.