Learning Material Segment Ranking via Knowledge Point Consistency
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Solution Overview
Problem
The proliferation of open education resources has led to difficulties in manually organizing and structuring learning materials, with existing ranking mechanisms being limited in effectively recommending relevant segments, often relying on keyword matching and text similarity, and failing to address the complexity of online learning materials.
Innovation Solution
A method is introduced to automatically analyze learning materials, extract knowledge points, and generate relevant segments by calculating window similarity and consistency measurements, allowing for the ranking of segments based on quality, type, length, and consistency, thereby improving the organization and recommendation of learning materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual organization methods are used to structure learning materials, then simplicity and ease of implementation are maintained, but the ability to effectively organize and recommend relevant segments deteriorates due to the proliferation of open education resources
Solution Approach 1:
The patent segments learning materials into discrete units called 'segments' based on knowledge points. Each segment is associated with specific knowledge points and can be independently ranked and recommended. This segmentation allows the system to handle large volumes of learning materials efficiently by breaking them down into manageable, searchable units that can be organized and retrieved without manual intervention.
Solution Approach 2:
The patent introduces multiple parameters for ranking segments including quality measurement, learning material type, segment length, and consistency measurement. These parameters enable automatic differentiation and ranking of segments based on their relevance and quality, replacing manual organization with a multi-dimensional automated evaluation system that can handle the proliferation of open education resources.
2Measurement precision
If existing ranking mechanisms rely on keyword matching and text similarity, then implementation simplicity is maintained, but the effectiveness of recommending relevant segments deteriorates due to inability to address material complexity
Solution Approach 1:
The patent introduces 'knowledge points' as intermediaries between learning materials and ranking mechanisms. Instead of directly comparing text similarity, the system extracts knowledge points from segments and uses them as the basis for ranking. This intermediary layer enables more precise measurement of segment relevance by focusing on conceptual content rather than surface-level text matching, thereby improving ranking accuracy while managing analysis complexity through structured knowledge extraction.
Solution Approach 2:
The patent replaces simple keyword matching mechanisms with a more sophisticated system that calculates window similarity, generates consistency measurements, and evaluates multiple ranking parameters. This substitution moves from mechanical text comparison to a systematic analysis approach that considers segment quality, type, length, and knowledge point consistency, thereby improving ranking effectiveness despite increased analysis complexity.
3Reliability
If comprehensive analysis of learning materials is performed to extract knowledge points and generate segments, then the quality of organization and recommendation improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary extraction of knowledge points from learning materials and pre-generates segments with their associated metadata (quality measurement, type, length, consistency). This preliminary action allows the system to have learning materials ready for rapid ranking and recommendation without performing comprehensive analysis at query time, thereby improving organization quality while reducing processing time for actual recommendations.
Solution Approach 2:
The patent implements a balanced approach where comprehensive analysis is performed to extract knowledge points and generate segments, but the full analysis is not repeated for every recommendation query. Instead, pre-computed segment data is used for rapid ranking, performing partial analysis only when necessary to update or refine segment information, thus achieving high organization quality without excessive processing time for each operation.
Data Source
AI summary
A method of automated ranking of segments of learning materials includes calculating a window similarity between first-window content of a first window in a learning material and second-window content of a second window in the learning material. The method may also include in response to the window similarity between the first-window content of the first window and the second-window content of the second window meeting a similarity threshold, generating a first segment that includes at least the first-window content and the second-window content. The method may include calculating a first-segment consistency measurement for the first segment based on a first-segment similarity between the first-segment content in the first segment and a knowledge point. The method may also include ranking the first segment with respect to one or more of the following: a second segment in the learning material and a third segment in a different learning material, wherein the ranking of the first segment is based on one or more of the following: a quality measurement, a learning material type of the learning material, a length of the first segment, and the first-segment consistency measurement of the first segment.