Automated Grading System Using Modular Policy Aggregation
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Solution Overview
Problem
Existing e-Learning systems face challenges in efficiently combining and assessing various grade objects to determine an individual's overall performance or proficiency, lacking flexibility and automation in grading processes.
Innovation Solution
A method and system for processing grade objects involve applying contributor policies, aggregators, and result policies to generate aggregate and result grade objects, allowing for weighted scoring, exclusion of high or low grades, and conversion to discrete values, which are stored, displayed, or sent to devices, facilitating flexible assessment structures and automated grading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple grade objects are combined to assess overall performance, then assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the grading process into distinct components: contributor policies (for processing individual grade objects), aggregators (for combining grades), and result policies (for final processing). Each component handles a specific aspect of the grading workflow, making the complex task of combining multiple grade objects manageable and configurable through modular policy applications.
2Adaptability or versatility
If flexible grading options are provided (weighted scoring, exclusion policies), then adaptability is improved, but processing complexity increases
Solution Approach 1:
The system implements dynamic grading configurations where contributor policies and result policies can be selectively applied based on assessment requirements. Policies such as weighting schemes, exclusion rules for high/low grades, and discrete value conversions can be activated or deactivated dynamically, allowing the system to adapt to different grading scenarios without requiring a completely different processing structure.
3Productivity
If automated grading processing is implemented, then productivity is improved, but implementation complexity increases
Solution Approach 1:
The automated grading system operates autonomously by automatically applying contributor policies to process grade objects, using aggregators to combine results, and implementing result policies to generate final assessments. The system self-manages the entire grading workflow without requiring manual intervention at each step, thereby improving productivity while the complexity is encapsulated within the automated processing logic.
Data Source
AI summary
Various embodiments are described herein that generally relate to a system and method for processing a plurality of grade objects to determine a value for an intermediate result grade object or a final result grade object according to an assessment structure. This may be accomplished by obtaining values for a plurality of grade objects and applying various policies and aggregator functions to these values based on the assessment structure.