Essay Analyzer for Higher Education Admissions Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Applicants, especially those applying for higher education study abroad programs, face challenges in crafting effective essays due to a lack of knowledge about appropriate content quality, language, and structure, often without access to timely guidance, which can impact their admissions prospects.
Innovation Solution
A machine-learning-based Essay Analyzer tool that utilizes natural language processing and machine learning to analyze, rate, and provide feedback on essays, offering suggestions for improvement by assessing language, structure, and content quality, and comparing the essay to a database of successful submissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a machine-learning-based Essay Analyzer is implemented to provide automated feedback, then the availability of guidance for applicants is improved, but the complexity of the evaluation system increases
Solution Approach 1:
The patent introduces an automated Essay Analyzer system that acts as an intermediary between applicants and human advisors. This machine-learning-based system processes essays, evaluates content quality, language proficiency, and structure, then provides detailed feedback and suggestions. The intermediary handles the complex evaluation tasks that would otherwise require human experts, making guidance accessible to all applicants regardless of resource availability.
Solution Approach 2:
The patent replaces the mechanical system of human review with an automated machine-learning-based evaluation system. The system uses natural language processing, topic modeling, and sentiment analysis to assess essays objectively and consistently. This substitution eliminates the need for manual review while maintaining evaluation quality, resolving the contradiction between accessibility and system complexity.
2Reliability
If detailed automated feedback is provided to improve essay quality, then the efficacy of essay submission is improved, but the computational resources required increase
Solution Approach 1:
The patent segments the essay evaluation process into distinct modular components: content quality assessment, language proficiency analysis, structure evaluation, topic modeling, and sentiment analysis. Each module independently processes specific aspects of the essay and generates targeted feedback. This segmentation allows the system to provide comprehensive detailed feedback while optimizing computational resource usage by processing different aspects in separate, efficient stages rather than requiring all resources simultaneously.
Data Source
AI summary
Systems and methods for the multifaceted analysis of a written work, such as an educational program admission essay, using machine learning, deep learning and natural language processing. Language, relevance, structure, and flows are evaluated for an overall impactful essay. Essay content is checked to evaluate whether the author has covered an essay's essential aspects. The essay is also analyzed for an effective structure for presenting details as per the essay type. The disclosure includes data preparation for the task, process of data tagging, feature engineering from the essay text, method for transfer learning and fine-tuning language model to adapt to the context of an essay. Finally, a process for building machine learning and deep learning models and technique for ensembling to use both models in combination is disclosed. The system may provide user-adapted feedback based on a persona created from the user profile.