A project by Experizon on Experizon.
You will design and implement a flexible grading engine capable of evaluating diverse student submissions, ranging from technical code repositories to design portfolios and accounting reports. Your task is to build a modular system that analyzes input text, file attachments, and metadata to output a standardized 0-100% grade. You will explore heuristic-based scoring, rubric mapping, and potentially integrate Natural Language Processing to normalize assessment across varying domains. Context Experizon receives a high volume of diverse project submissions. Manually grading these requires significant domain expertise and time. We need to automate the preliminary assessment process to provide students with rapid feedback while ensuring fairness and consistency across different industries, from software engineering to creative design. Objectives By completing this project, you will: - Learn to design adaptable, domain-agnostic software architectures - Implement automated evaluation logic for complex, unstructured data - Gain experience in rule-based systems and potential AI/NLP model integration - Develop skills in data validation and performance benchmarking for automated tools Requirements Prerequisites: - Python or JavaScript - Understanding of data structures and JSON schema design - Familiarity with basic text analysis or pattern matching techniques - Ability to translate qualitative rubrics into quantitative algorithms
Final deliverables include: - A modular Python/Node.js grading function prototype - Domain-specific rubric integration framework (e.g., JSON schemas for different tasks) - Unit testing suite demonstrating accuracy across 3+ sample project types - Technical documentation detailing the scoring logic and scalability approach - Evaluation report comparing machine-generated grades against manual baselines