Multi-LMS Course Assignment via Difficulty Clustering
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Solution Overview
Problem
Existing learning management systems (LMSs) face challenges in comparing and consolidating courses from different platforms due to varying difficulty labels and lack of standardized criteria, making it difficult to create an effective learning path for users across multiple LMSs.
Innovation Solution
A multi-LMS system that receives difficulty and expertise scores from various LMSs, generates overall difficulty measures, clusters courses, and creates a sequence of courses tailored to individual users based on their expertise and feedback, ensuring a progressive learning path by uniformly comparing and labeling courses across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If courses from multiple LMSs are consolidated without standardized difficulty measures, then the quantity of available courses increases, but the precision of course difficulty comparison deteriorates
Solution Approach 1:
The patent transforms subjective difficulty labels from multiple LMSs into a standardized numerical difficulty score through aggregation of user feedback. User feedback on course difficulty is collected and processed to generate a unified difficulty parameter that enables precise comparison across different LMS platforms while maintaining the ability to consolidate courses from diverse sources
2Adaptability or versatility
If difficulty labels from different LMSs are used as-is, then the diversity of course sources is maintained, but the reliability of difficulty assessment deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that collects user feedback from multiple LMSs and generates aggregated difficulty scores. This intermediary mechanism reconciles the diversity of course sources by translating various LMS difficulty labels into a common assessment framework based on actual user experiences, thereby improving reliability without sacrificing source diversity
Solution Approach 2:
The system implements feedback loops where user responses about course difficulty are continuously collected and used to refine difficulty assessments. This feedback mechanism allows the system to maintain reliable difficulty ratings across diverse LMS sources by adapting to actual user perceptions rather than relying on inconsistent platform-specific labels
3Speed
If courses are selected without considering user expertise level, then the speed of course assignment increases, but the effectiveness of skill development deteriorates
Solution Approach 1:
The patent performs preliminary assessment of user expertise levels and course difficulty ratings before making course assignments. By pre-calculating compatibility between user skill levels and course difficulties based on accumulated feedback data, the system can quickly assign appropriate courses without compromising the effectiveness of skill development
4Measurement precision
If manual curation of learning paths is performed, then the accuracy of personalized learning sequences improves, but the productivity of course assignment deteriorates
Solution Approach 1:
The patent implements a self-service system where the automated course recommendation engine uses aggregated user feedback and expertise data to generate personalized learning paths without manual intervention. The system serves itself by continuously learning from user interactions and automatically optimizing course sequences, thereby maintaining high accuracy while achieving scalable productivity
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage media, for course assignment by a multi-learning management system. The system can receive data from a variety of individual learning management systems offering different courses. The system can use feedback data of a user base for the system to cluster courses by predicted difficulty, and generate, from the clusters, a sequence of courses for a target user. The sequence of courses can include at least one course from each cluster, with courses from a first cluster with a lower overall difficulty measure preceding courses in a second cluster with a higher overall difficulty measure in the sequence, wherein the starting cluster can be calculated according to the estimated level of the target user.