Map-Based Educational Data Analysis System
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Solution Overview
Problem
Current educational big data analysis methods are inefficient and lack accuracy in integrating and analyzing complex educational data, failing to provide comprehensive insights for educational management and personalized learning guidance.
Innovation Solution
A method and system for analyzing educational big data using maps, which involves acquiring and classifying educational resource data, constructing theme map layers, analyzing data based on geotags, and combining learning preferences to provide personalized learning recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional educational big data analysis methods are used, then data processing can be performed, but the analysis accuracy and comprehensiveness are insufficient
Solution Approach 1:
The patent segments educational big data into multiple thematic categories (student development, teaching quality, school management, etc.) and processes each category separately through dedicated analysis modules. This segmentation approach improves analysis accuracy by focusing on specific educational aspects while managing complexity through modular processing rather than attempting to analyze all data simultaneously.
Solution Approach 2:
The patent introduces map visualization as an intermediary layer between raw educational data and analysis results. This intermediary transforms complex multi-dimensional educational data into intuitive spatial representations, enabling accurate analysis of educational resource distribution, student performance patterns, and school management metrics without being overwhelmed by data complexity.
2Loss of information
If multiple data sources are integrated for comprehensive analysis, then analysis completeness improves, but processing efficiency decreases
Solution Approach 1:
The patent divides comprehensive educational data into distinct thematic segments (student development data, teaching quality data, school management data, etc.) that can be processed independently through specialized modules. This maintains information completeness by preserving all data categories while improving efficiency through parallel processing of segmented data rather than sequential analysis of the entire dataset.
Solution Approach 2:
The patent implements selective data processing where only relevant data subsets are processed based on specific analysis needs. For example, when analyzing student performance, only student development data and related teaching quality data are processed rather than all educational data, thereby maintaining necessary information completeness while significantly improving processing efficiency.
3Measurement precision
If detailed classification of educational data is performed, then analysis precision improves, but system complexity increases
Solution Approach 1:
The patent implements detailed classification of educational data into multiple hierarchical levels (first-level categories like student development, teaching quality; second-level subcategories like academic performance, behavioral patterns). This segmentation achieves high analysis precision by enabling granular examination of specific educational aspects while managing system complexity through a structured hierarchical framework rather than unorganized detailed classification.
Solution Approach 2:
The patent designs a universal data processing framework that handles multiple types of educational data through standardized procedures. The same classification and processing mechanisms are applied across different data categories (student data, teaching data, management data), achieving detailed classification precision while reducing system complexity through reusable universal components rather than separate specialized systems for each data type.
Data Source
AI summary
The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.