Assessment Computing Device Normalizing Diverse Score Scales
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Solution Overview
Problem
Current systems lack an efficient method to aggregate, normalize, and interpret competency-based assessment data from diverse sources, making it difficult to provide comprehensive mastery scores and track student learning progression across multiple standards and educators.
Innovation Solution
The system integrates an image capture device, assessment computing device, and network connectivity to collect and normalize scores from various sources, including third-party tools and educator assessments, using algorithms to determine mastery levels and generate user interfaces that display student progress and suggest personalized activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If assessment data is collected from multiple diverse sources (third-party tools, educator assessments, in-classroom activities), then the comprehensiveness of mastery scores is improved, but the complexity of aggregating and normalizing data increases
Solution Approach 1:
The assessment computing device serves as an intermediary that receives assessment data from multiple diverse sources (third-party tools, educator assessments, in-classroom activities), normalizes the data to a common scale, and aggregates it into comprehensive mastery scores. This intermediary component handles the complexity of data integration while presenting a unified view to users.
Solution Approach 2:
The system employs a universal normalization algorithm that can process assessment data from various sources using different scales and formats, converting them all to a standardized mastery scale. This multi-functional approach allows the same system to handle diverse assessment types without requiring separate processing mechanisms for each source.
2Measurement precision
If scores from different scales are normalized to a common mastery scale, then comparability of assessment data is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies parameter transformation by converting scores from different scales (e.g., percentage, letter grades, rubric scores) into a standardized mastery scale parameter. This parameter change enables direct comparison of assessment data while using efficient mathematical transformation algorithms that minimize processing time.
3Loss of information
If comprehensive learning progression analysis is provided across multiple standards and educators, then the value of assessment information is improved, but the complexity of data interpretation increases
Solution Approach 1:
The system segments the comprehensive assessment data into discrete learning progressions for individual standards, allowing educators to view mastery levels for each specific standard separately. This segmentation breaks down the complex overall assessment into manageable, interpretable units while maintaining the ability to aggregate across multiple standards when needed.
Data Source
AI summary
A system includes an image capture device and an assessment computing device. The image capture device is configured to capture an image of an object and a first score associated with the object on a first scale. The assessment computing device is configured to receive the image of the object and the first score from the image capture device, and to receive a second score on a second scale that is different from the first scale from a third party assessment source computing device. The assessment computing device is also configured to normalize the second score to a particular scale to provide a normalized second score, and to determine a level of mastery based at least partially on the first score and the normalized second score. The assessment computing device is configured to generate a user interface with information indicating the level of mastery.