Ordered Expert Lists for Shared Digital Documents
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Solution Overview
Problem
Online learning platforms fail to foster a sense of community and facilitate quick question resolution among students, making it difficult for students to establish informal learning groups with knowledgeable peers.
Innovation Solution
A method and apparatus for generating an ordered user expert list for shared digital documents, where expert students are ranked based on their annotations and test scores, allowing users to collaborate and import relevant annotations from top-ranked experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If students use online learning platforms to access shared digital documents, then information accessibility is improved, but community engagement and knowledge sharing deteriorate
Solution Approach 1:
The patent introduces an intermediary system that automatically identifies expert students and generates ordered lists of experts based on their annotations and contributions. This intermediary mechanism facilitates knowledge sharing by connecting students with relevant experts without requiring manual community building, thus maintaining information accessibility while improving community engagement.
Solution Approach 2:
The system enables self-service by allowing students to automatically access expert lists and annotations without manual intervention. Students can independently find and interact with experts relevant to their learning needs, eliminating the need for formal study group formation while maintaining community connectivity.
2Ease of operation
If students attempt to establish informal learning groups manually, then community bonding is improved, but time consumption and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-identifying expert students and organizing them into ordered lists based on their annotations and contributions. This preparation work is done in advance, so when students need expert help, the matching is already completed, eliminating the time-consuming process of manual group formation while preserving community bonding opportunities.
Solution Approach 2:
The system uses feedback mechanisms where student interactions with annotations and experts are tracked and used to refine future expert recommendations. This continuous feedback loop improves the accuracy of expert matching over time, making community engagement more efficient without sacrificing the quality of student-expert connections.
3Measurement precision
If expert students are identified and ranked based on annotations, then knowledge sharing quality is improved, but system complexity increases
Solution Approach 1:
The patent changes parameters by using multiple measurable indicators (annotation quality, contribution frequency, expertise relevance) to assess student knowledge levels. These parameter changes enable precise expertise assessment without requiring complex evaluation frameworks, as the system leverages existing interaction data to generate simple, actionable expert rankings.
Data Source
AI summary
A computer implemented method and apparatus for generating ordered user expert lists for a shared digital document. The method comprises accessing a digital document, wherein the digital document relates to one or more topics; generating a list of expert students, wherein the expert students have authored one or more annotations relating to a topic similar to a topic in the digital document; ordering the list of expert students according to a rank, wherein the rank identifies a level of expertise of the expert students; and presenting the ordered list of expert students, where the ordered list comprises a pre-defined number of expert students with a level of expertise meeting a predefined threshold.