Machine Learning System for Student Competency Assessment

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Solution Overview

Problem

Current educational technologies lack intelligent systems to monitor and measure student competencies developed in online programs, failing to provide targeted resources for improvement and accurate assessments of skill development.

Innovation Solution

A computer-implemented system using machine learning that processes student interaction data, removes irrelevant information, and compiles neural network models to predict skill assessments, integrating with online learning services to provide personalized resource recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If universities provide credits based on hours of lessons attended, then the credit distribution process is simple and straightforward, but the accuracy of measuring actual skill development and competency improvement deteriorates

Engineering Contradiction:

Improveease of credit distributionVSAvoidprecision of skill development measurement

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces the mechanical system of manual teacher assessment with an automated machine learning system that uses natural language processing and neural networks to objectively measure student competencies. The system automatically analyzes student interactions, discussions, and assignments to generate competency assessments, eliminating the need for time-consuming manual evaluation while providing more precise and consistent measurements of actual skill development.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables students to self-assess their own competencies through automated analysis of their learning activities and interactions. The machine learning model processes student-generated content and provides personalized competency feedback, allowing students to track their own skill development without requiring constant teacher intervention, thus improving measurement precision while maintaining ease of operation.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If teachers manually assess and measure student competencies, then personalized feedback can be provided, but the time consumption and workload increase significantly

Engineering Contradiction:

Improvepersonalization of feedbackVSAvoidtime for assessment

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system provides continuous automated assessment of student competencies throughout the learning process, rather than periodic manual assessments. The machine learning model continuously processes student activities and provides ongoing feedback, enabling personalized competency tracking without interrupting the learning flow or requiring teacher time investment.

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If comprehensive student interaction data is collected for competency assessment, then the accuracy of skill measurement improves, but the complexity of data processing and system architecture increases

Engineering Contradiction:

Improveaccuracy of competency assessmentVSAvoidcomplexity of data processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex data processing task into distinct modular components: data collection modules that gather specific types of student interactions, preprocessing modules that clean and organize data, analysis modules that apply specific machine learning algorithms to different data types, and output modules that generate competency assessments. This segmentation allows the system to handle comprehensive data while maintaining manageable complexity through organized, reusable components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal machine learning models and algorithms that can process multiple types of student interaction data (discussions, assignments, quizzes, collaborations) through a common framework. The neural network architecture is designed to handle diverse data formats and interaction types uniformly, reducing system complexity by avoiding the need for separate specialized processing systems for each data type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11551570B2Systems and methods for assessing and improving student competencies

Publication Date: 2023.01.10 SMARTHINK SRL

AI summary

A skills learning method for a student gathers objective data relating to the student in response to various stimuli, and produces a predicted feedback units as a function of the objective data using a machine learning-base classifier. The method can include training a neural network using objective data of student interactions and associated subjective assessments of a skill of each objective data. The method includes receiving a new dataset with objective data of a new student and an associated subjective assessment of a skill of the first student represented by the new objective data. A predicted assessment of the skill of the new objective data is calculated by inputting the new objective data into the neural network. The method can include updating the neural network by combining the initial dataset and the new dataset and recompiling the neural network to fit the model dataset based on a learning algorithm.