Skill Assessment System Using Probabilistic Inference for Partial Responses
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Solution Overview
Problem
Current skill assessment methods are inadequate in measuring users' skill levels on specific subjects and subtopics, as they often require numerous questions, which can be time-consuming and incomplete, especially when users do not answer all questions, leading to an incomplete understanding of their skills and educational needs.
Innovation Solution
A system and method that extrapolate skill levels from a subset of assessment questions by using probabilistic predictions based on question mapping data, determining skill gaps, and providing customized instructional content to users by aggregating question responses, calculating probabilities using Bayes' Theorem, and mapping content to topics using term frequency-inverse document frequency and cosine similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous questions are asked to determine user skill level, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs skill assessment using only a subset of available questions rather than requiring users to answer all questions. By selecting and administering a partial set of questions strategically, the system achieves sufficient measurement precision while significantly reducing the time users must invest in the assessment process.
Solution Approach 2:
The system pre-calculates and stores question mapping data that represents relationships between questions and skills before assessments are administered. This preliminary preparation enables the system to quickly determine skill levels from limited user responses without requiring extensive real-time computation, thus reducing assessment time while maintaining precision.
2Loss of time
If users do not answer all questions, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The system introduces question mapping data as an intermediary element that connects answered questions to unanswered questions. This mapping data allows the system to infer skill levels for topics covered by unanswered questions based on patterns from answered questions, thereby maintaining measurement precision even when users provide incomplete responses.
Solution Approach 2:
The system uses responses to answered questions as proxies or copies to infer information about unanswered questions. By analyzing the relationship between answered and unanswered questions through question mapping data, the system can estimate what users would have answered had they completed all questions, preserving measurement accuracy without requiring full completion.
3Ease of operation
If broad skills are measured, then ease of operation is improved, but measurement precision for specific subjects deteriorates
Solution Approach 1:
The system segments the assessment into broad skill categories and specific subject areas using question mapping data. This segmentation allows the system to first evaluate overall skill levels broadly and then drill down into specific subjects and subtopics, providing both ease of operation for broad assessment and precision for specific subject measurement simultaneously.
Solution Approach 2:
The system applies different levels of assessment depth to different skill areas based on question mapping data. For topics where users have answered sufficient questions, the system provides detailed local precision. For other areas, it uses inferred data to maintain adequate measurement quality without requiring exhaustive questioning across all subjects, thus balancing operational ease with measurement precision.
Data Source
AI summary
A system and method for providing customized content recommendations to a user based on extrapolated data is described. The system may receive an answer to a question from a user and determine a second unanswered question having a threshold relationship level with the question. The system may compute a predicted probability that the user would answer the second question correctly, calculate a first comparative skill level of the user among a cohort of similar users, and rank the first comparative skill level of the user against a second comparative skill level of the cohort to determine a skill gap of the user. Further, in some implementations, the system may generate a search query based on the skill gap of the user, and determine recommended content customized to the skill gap of the user.