Dynamic Education Planning System for Non-Traditional Learners

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional educational techniques fail to provide holistic support for individuals pursuing non-traditional paths into software engineering and other fields, lacking the adaptive skills-based learning necessary for success in competitive industries, especially for those with interdisciplinary backgrounds or second-career entrants.

Innovation Solution

A computer-implemented method and system using machine learning to generate dynamic education plans by processing fellow skill graphs and electronic calendar objects, predicting sessions, and displaying them on a roadmap GUI, incorporating mentoring and skills matching to address competency gaps and career goals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional educational paths are followed, then students gain traditional credentials and structured learning, but non-traditional learners (interdisciplinary backgrounds, second-career entrants) lack holistic support and adaptive skills-based learning

Engineering Contradiction:

Improveadaptability to non-traditional learning pathsVSAvoidaccessibility for non-traditional students

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system dynamically adapts education plans based on individual fellow skill graphs, career goals, and competency assessments. The machine learning model continuously processes skill data and calendar objects to generate personalized learning pathways that evolve as fellows progress, making the educational system flexible and responsive to non-traditional learners' needs

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system enables non-traditional students to self-assess their skills through competency evaluations and skill graph updates. Fellows can independently track their progress, identify gaps, and access tailored learning resources without requiring traditional academic intermediaries, empowering them to navigate their own educational journeys

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If existing online courses are used, then students can learn specific curriculum facets remotely, but they lack holistic support networks and comprehensive skills development

Engineering Contradiction:

Improveavailability of online coursesVSAvoidcompleteness of skills acquisition

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The system merges multiple educational components into a unified platform: skill assessments, personalized curriculum recommendations, mentor matching, career goal alignment, and progress tracking are integrated into a cohesive education plan generation system that delivers comprehensive skills development beyond isolated online courses

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The education plan system serves multiple functions simultaneously: it assesses current skills, identifies competency gaps, recommends learning resources, schedules sessions, matches mentors, and tracks progress toward career goals. This multi-functional approach replaces the need for separate educational services with a single comprehensive platform

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Manufacturing precision

If conventional educational curricula are followed, then students meet standardized requirements, but they fail to develop the specific skill sets needed for competitive industries

Engineering Contradiction:

Improvestandardization of educationVSAvoidindustry readiness

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system changes the parameters of education planning from fixed curricular requirements to dynamic skill-based metrics. Instead of following predetermined course sequences, the system adjusts learning parameters based on individual skill levels, career objectives, and industry requirements, enabling personalized pathways that directly target industry-ready competencies

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20230410237A1Dynamic education planning methods and systems

Publication Date: 2023.12.21 FORMATION LABS INC

AI summary

A method includes receiving fellow skill graphs and electronic calendar objects; processing the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and displaying the sessions. A computing system includes a processor; and a memory having stored thereon executable instructions that, when executed by a processor, cause the computing system to: receive fellow skill graphs and electronic calendar objects; process the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and display the sessions. A non-transitory computer-readable medium includes executable instructions that, when executed, cause a computer to: receive fellow skill graphs and electronic calendar objects; process the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and display the sessions.