Machine Learning Student Journey Mapping
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Solution Overview
Problem
Educational institutions face challenges in documenting and analyzing student progress due to the vast amounts of unstructured data, making it difficult to identify relevant information and predict student journeys effectively.
Innovation Solution
A method involving a computer network-based system that normalizes unstructured student data using machine learning classification models to identify friction points and achievement points, generating predictive information about student progress, which is then transmitted to administrators for actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unstructured data is manually reviewed to extract useful information, then information accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated natural language processing system. The NLP model automatically extracts and classifies information from unstructured mentor notes, eliminating the need for human reviewers to manually read through thousands of notes while maintaining high accuracy through machine learning algorithms.
Solution Approach 2:
The patent introduces an NLP-based intermediary system that acts as a mediator between raw unstructured data and actionable insights. This intermediary automatically processes mentor notes, identifies friction and achievement points, and transforms unstructured text into structured data that can be directly used for student journey mapping without manual intervention.
2Loss of information
If comprehensive student progress documentation is attempted across all students, then completeness of documentation is improved, but system complexity increases
Solution Approach 1:
The patent segments the overwhelming task of documenting all student progress into manageable units by focusing on specific friction and achievement points identified through NLP analysis. Instead of attempting to process all student data uniformly, the system divides the problem into targeted analysis of specific data patterns and themes across the student population.
Solution Approach 2:
The patent creates simplified copies or representations of complex student journey data through standardized friction and achievement point classifications. The NLP system generates structured summaries and journey maps that capture essential progress information without requiring the full complexity of raw unstructured data to be maintained and processed.
3Productivity
If unstructured data is analyzed without standardization, then data processing speed is improved, but consistency of analysis deteriorates
Solution Approach 1:
The patent transforms unstructured text data into standardized parameters and categories through NLP processing. The system converts varied mentor note formats into consistent friction and achievement point classifications, enabling both rapid processing and uniform analysis across all students while maintaining data integrity through structured output formats.
Data Source
AI summary
Generating a predictive mapping of an educational journey of a student. The method includes receiving, over a computer network, from a database of student records, unstructured data about a particular student of the educational institution. The unstructured data is normalized to classify the unstructured data consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications. Based on the classifications of the unstructured data, A plurality of friction points that hinder a particular student's progress in the educational journey and a plurality of achievement points that promote the particular student's progress in the educational journey are identified. Using the friction points and achievement points, prediction information is generated of the particular student's progress in the educational journey. The prediction information is consistent with a machine learning prediction model. The prediction information is transmitted over the computer network to an administrator machine.