Mastery Assessment Data Normalization Across Learning Platforms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing educational systems struggle to consolidate and accurately assess student mastery data from diverse online learning tools and external assessments, leading to inefficiencies and inconsistencies in data management, and manual interpretation is prone to errors.
Innovation Solution
A system that centralizes and normalizes user educational data from various platforms, calculates reliability and recency scores, and categorizes mastery levels into 'unknown', 'learning', and 'confirmed' states, providing a unified view accessible via an API for personalized learning experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If educators manually interpret data from various educational sources, then they can understand student progress, but the process is prone to errors and inconsistencies
Solution Approach 1:
The patent introduces an intermediary system comprising a collector, normalization module, and evaluator that automatically processes and consolidates mastery data from multiple educational sources. This intermediary layer transforms raw data from diverse platforms into standardized, normalized data structures, eliminating manual interpretation errors while maintaining data accuracy through systematic processing and evaluation algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of data interpretation with an automated computational system. The collector automatically gathers data, the normalization module standardizes formats, and the evaluator algorithmically determines mastery levels, substituting human manual work with automated mechanical processes that reduce errors and improve consistency.
2Adaptability or versatility
If multiple educational platforms are integrated to provide comprehensive student data, then a complete view of student progress is achieved, but the system complexity increases
Solution Approach 1:
The normalization module is designed with universal functionality to handle data from multiple educational platforms (Canvas, Blackboard, Moodle, Google Classroom, etc.). It performs multiple functions including data collection, format standardization, validation, and normalization across different platform protocols, enabling the system to adapt to various platforms without requiring separate processing logic for each.
Solution Approach 2:
The system changes parameters by transforming diverse data formats from different platforms into a unified normalized data structure. The normalization module adjusts data parameters (formats, schemas, identification methods) to match a standard model, allowing compatible integration of multiple platforms while maintaining system manageability through consistent parameter transformation rules.
3Loss of information
If all educational data is collected and stored centrally, then a unified view of student mastery is achieved, but data management becomes inefficient
Solution Approach 1:
The patent segments the centralized data management system into distinct functional modules: a collector for data gathering, a normalization module for standardization, and an evaluator for mastery determination. This segmentation allows each module to handle specific tasks efficiently, reducing the complexity of managing all data centrally while maintaining a unified view through the coordinated output of segmented components.
Solution Approach 2:
The system extracts only the essential mastery-related data from comprehensive educational platforms, storing it in a normalized format. Rather than managing all raw data from multiple sources, the collector and normalization module extract and retain only the critical information needed for mastery assessment, improving data management efficiency while preserving the completeness of student progress information.
Data Source
AI summary
A mastery level determining method to assess user mastery of educational skills by centralizing and processing data from diverse educational platforms is disclosed. The method involves collecting user educational data from various sources such as online learning platforms, external assessments, and internal quizzes. This data is then normalized to adapt to the educational standards and ingested into a structured mastery framework. The recent and reliable data is evaluated to determine skill mastery states, prioritizing newer and more reliable information. Subsequently, reliability and recency scores are calculated and assigned to each educational data. The method further categorizes user mastery skills into distinct states, providing an overview of the user's knowledge. The simplified mastery states and associated tags are accessible to the user, facilitating personalized learning experiences and assessments within the online learning platforms.