Answer Correction via Position Alignment Algorithms
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Solution Overview
Problem
Existing answer correction methods fail to accurately recognize the correct position of answers filled by students on test papers, leading to difficulties in correcting assignments and exams.
Innovation Solution
An answer correction method that involves searching for a target test paper in a question bank, marking answer areas on both the test paper and the target paper, using algorithms like coherent point drift and bipartite graph minimum weight matching to adjust and match answer positions, ensuring accurate correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If students fill answers freely on test papers, then students can write answers in their preferred format, but answer positions may extend beyond effective areas or occupy other questions' answering areas making recognition difficult
Solution Approach 1:
The system performs preliminary action by predicting the standard answer position before actual answer recognition. It establishes a reference answer position based on the question position and standard answer layout, then uses this predicted position as a starting point for subsequent answer recognition and matching operations, enabling accurate identification even when students write answers in non-standard locations
Solution Approach 2:
The system introduces an intermediary element - the answer position prediction module - that acts as a mediator between the question position and the student's actual answer. This intermediary predicts where the answer should be located based on structural relationships, then guides the recognition process to find the actual answer near the predicted position, effectively bridging the gap between standard and non-standard answer placements
2Reliability
If answer positions are not accurately recognized, then correction process becomes complex and error-prone, but implementing complex position recognition algorithms increases system complexity
Solution Approach 1:
The system segments the answer correction process into distinct modules: question position recognition, standard answer position prediction, actual answer position recognition, and answer matching. Each module handles a specific sub-task with focused complexity, rather than implementing one monolithic complex algorithm, thereby improving reliability while managing system complexity through functional decomposition
Solution Approach 2:
The system performs preliminary prediction of standard answer positions before attempting to recognize actual answers. This preliminary action establishes expected position ranges and structural constraints that guide the subsequent answer recognition process, simplifying the matching task by reducing the search space and providing reference frameworks for alignment
Data Source
AI summary
The disclosure provides an answer correction method and a device, including: determining the target test paper that matches the test paper to be corrected; marking the area of each answer in the test paper to be corrected as the first answer set, and marking the area of each answer in the target test paper as the second answer set; matching each answer area in the first answer set and the second answer set, and adjusting the position of the answer area in the first answer set on the test paper to be corrected; for each answer area in the second answer set, determining the target answer area in the first answer set according to the position information of the answer area on the target test paper, and correcting the answer in the determined target answer area according to the answer in the answer area. The disclosure can solve the problem in the related art that the accurate position of the answer filled by the student cannot be identified, thereby affecting the correction of the answer.