Essay Evaluation System Using Structural Segmentation for Precision Feedback
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Solution Overview
Problem
Existing systems for automatically evaluating essays provide only holistic scores, lacking detailed structural feedback, which limits their ability to offer tailored feedback suitable for various essay types and lengths, hindering effective writing improvement for learners.
Innovation Solution
A system and method that utilize structural modeling to analyze essay structures and relationships, generating detailed feedback by dividing essays into predetermined units, extracting major features, determining essay types, and applying structure tagging to provide comprehensive evaluation and feedback for each structure unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If holistic scoring is used for essay evaluation, then evaluation speed is improved, but evaluation precision deteriorates
Solution Approach 1:
The essay evaluation process is segmented into multiple dimensions including structure analysis (dividing into introduction, body, conclusion), content evaluation, language quality assessment, and coherence analysis. Each dimension is evaluated separately by dedicated analysis modules, allowing comprehensive precision while maintaining automated efficiency through parallel processing of multiple evaluation aspects.
2Measurement precision
If structural analysis is added to essay evaluation, then evaluation precision is improved, but system complexity increases
Solution Approach 1:
The system divides structural analysis into discrete components: paragraph division module that identifies introduction/body/conclusion sections, sentence-level structure analysis, and hierarchical organization assessment. This segmentation allows the complex structural evaluation to be handled through multiple simple, specialized modules rather than one complex monolithic system.
Solution Approach 2:
A structure analysis module serves as an intermediary between the essay input and the evaluation engine. This intermediary processes the raw essay text, extracts structural features, and transforms them into standardized data formats that the evaluation engine can process, thereby managing complexity through modular architecture with clear interfaces.
3Loss of information
If detailed structural feedback is provided, then feedback quality is improved, but information processing load increases
Solution Approach 1:
The system provides differentiated feedback at multiple levels: global structural feedback (introduction-body-conclusion organization), paragraph-level feedback (coherence and development), and sentence-level feedback (grammar and expression). Each level receives appropriately detailed feedback based on its specific needs, avoiding unnecessary processing overhead while maximizing feedback quality where it matters most.
Solution Approach 2:
The system extracts only the most relevant structural features and evaluation metrics from the comprehensive analysis, separating essential feedback information from redundant data. By extracting key structural patterns and focusing evaluation on critical dimensions, the system reduces information processing load while maintaining high feedback quality through selective presentation of the most valuable evaluation insights.
Data Source
AI summary
Disclosed are a system and method for automatically evaluating an essay. The system includes a structure analysis module configured to divide learning data and learner essay text in a predetermined structure analysis unit, generate structure tagging information for each structure analysis unit, and structure the learning data and the learner essay text by attaching the structure tagging information to the learning data and the learner essay text, a learning module configured to generate an essay evaluation model through learning by using essay text that is included in the structured learning data and the structure tagging information as an input value and using an evaluation score that is included in the structured learning data as a label, and an evaluation module configured to generate essay evaluation results using the essay evaluation model.