Context-Aware Query Processing for Educational Search Relevance
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Solution Overview
Problem
The existing search technologies face challenges in accurately retrieving relevant information for learners due to the inadequacy of textual representations in capturing the precise query intent, leading to inappropriate knowledge assumptions and frustration from irrelevant search results.
Innovation Solution
The system processes search queries by utilizing educational contexts and temporal information to assign weights to concepts, thereby identifying relevant content objects that align with the learner's current course topics and schedule, ensuring more accurate and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search query processing is used, then the search system is simple and fast, but the search results are not relevant to the learner's educational context and knowledge level
Solution Approach 1:
The system performs preliminary actions by collecting and storing learner profile information, course enrollment data, and schedule information before the search query is submitted. This pre-collected contextual data is then used to enhance the query processing, allowing the system to understand the learner's educational context and knowledge level without adding significant complexity to the actual search operation.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the simple search query and the complex contextual information. This intermediary layer processes the query by integrating learner profile data, course information, and schedule data to generate contextualized search results, thereby improving relevance without requiring fundamental changes to the core search infrastructure.
2Reliability
If contextual information is integrated into query processing, then search result relevance is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing learner profile information, course enrollment data, and schedule information before the search query is submitted. This pre-collected contextual data is then used to enhance the query processing, allowing the system to understand the learner's educational context and knowledge level without adding significant complexity to the actual search operation.
Solution Approach 2:
The system applies partial action by selectively using only the most relevant contextual information for each query rather than processing all available data. The contextualization process focuses on key factors such as current course topics, scheduled activities, and learner knowledge level, ignoring less relevant information to maintain efficient processing speeds while still improving result relevance.
3Loss of information
If only textual query representation is used, then the input method is simple, but the query cannot capture the learner's precise intent and knowledge base
Solution Approach 1:
The patent merges the simple textual query with rich contextual information from multiple sources including learner profiles, course enrollments, and schedules. By combining these diverse information sources, the system creates a comprehensive understanding of the learner's intent and knowledge level, recovering information that would be lost if only the textual query were used.
Solution Approach 2:
The system performs preliminary actions by collecting and storing learner profile information, course enrollment data, and schedule information before the search query is submitted. This pre-collected contextual data is then used to enhance the query processing, allowing the system to understand the learner's educational context and knowledge level without adding significant complexity to the actual search operation.
Data Source
AI summary
Techniques can construct a learner's educational context (e.g., course enrollments, subject-matter interests, and/or activity involvements) and tailor query processing using the educational context. For a given query, each concept in a set of concepts can be assigned a weight. The weight can depend on a query term in the query. For example, for a query including “North America”, a “geography” concept and a “history” concept can be determined to be related to the query, and weights can be influenced accordingly. Weights can also depend on a user's educational context (e.g., such that the “geography” weight is higher when a learner is enrolled in a geography course). A query time can also be analyzed in view of schedule data (e.g., indicating when particular topics are to be studied in a course). Weights can further depend on which concepts are recently, currently or will soon be of interest based on the schedule.